Generation and management of data insights to aid collaborative media object generation within a collaborative workspace

ABSTRACT

In the present disclosure, a collaborative workspace fosters content creation between users in a synchronous and/or asynchronous manner by enabling automatic generation and management of collaborative media objects that automatically combine content from a plurality of users into a single media object. To improve processing efficiency and user productivity, the present disclosure further provides processing that applies trained artificial intelligence (AI) processing that is adapted to automatically generate representations of data insights for management of a collaborative media object within the collaborative workspace. Data insights are generated pertaining to feature management of an exemplary application/service (e.g., video discussion application/service) including features for generating and managing a collaborative media object. Signal data associated with users of a collaborative session (e.g., within a collaborative workspace) is detected and analyzed. Contextually relevant data insights are generated, where representations of data insights are provided for rendering through the collaborative workspace (e.g., GUI representation thereof).

BACKGROUND

With “remote everything” being the new normal, online collaboration ispivotal to maintaining the same productivity that users traditionallyhave via in-person interactions. However, current solutions for videocollaboration are limited in synchronous and asynchronous collaborationscenarios. For instance, users can either co-record a single video atthe same time or are limited to sharing files with one another andhaving one user stitch together a final video product. This isespecially cumbersome for users when they are working with a pluralityof content portions (e.g., videos) from different users and manuallycreating a combined work product (e.g., school assignments, workpresentations). A lot of time and manual actions (processing operations)are required to be executed to combine content in a final work product.As such processing efficiency for both computing devices as well asapplications/services executing thereon can be greatly improved incollaborative scenarios.

Furthermore, improvements in processing capabilities have made userscome to expect more intelligent applications/services that can adapt toa context in which a user is working. Users have also come to expectinstantaneous processing results that reduce latency when creatingcontent. However, traditional applications/services do not typicallyprovide intuitive graphical user interfaces (GUIs) that assist userswith task completion relative to an operational context that a user isexperiencing. This creates a disadvantage in collaborative scenarios,especially when users have to execute a plurality of manual actions toaccomplish a task. As such, improvements in applications/services,including GUIs, can greatly enhance a user experience and overallproductivity, especially in collaborative instances that involvemultiple users.

SUMMARY

For resolution of the above technical problems and other reasons, thereis a technical need for a collaborative workspace that fosters contentcreation between users in a synchronous and/or asynchronous manner.Uniquely, an exemplary collaborative workspace, presentable through aGUI of an application or service, is adapted to enable automaticgeneration and management of collaborative media objects. Collaborativemedia objects are generated that automatically combine content from aplurality of users into a single media object. This is extremelybeneficial in technical scenarios where users are creating projects,assignments, presentations, etc., by removing the need for users tomanually stitch together and combine content to create a final product.In some technical instances, the collaborative workspace is adapted fora video discussion application/service, where users create one or morevideo clips (e.g., video feeds, live camera feeds) in response to aposted topic.

To improve processing efficiency and user productivity relative to acollaborative workspace, the present disclosure further providesprocessing that applies trained artificial intelligence (AI) processingthat is adapted to automatically generate representations of datainsights for management of a collaborative media object within thecollaborative workspace. Data insights may be generated pertaining tofeature management of an exemplary application/service (e.g., videodiscussion application/service) including features for generating andmanaging a collaborative media object. Signal data associated with usersof a collaborative session (e.g., within a collaborative workspace) maybe detected and analyzed. While a plurality of different types of signaldata are applicably described herein, detected signal data comprisesapplication-specific signal data pertaining to user interactions ofusers within a collaborative workspace (e.g., of a video discussionapplication or service). Analysis of signal data leads to adetermination as to a context of users within a collaborative workspace.Contextually relevant data insights may then be generated, whererepresentations of data insights are provided for rendering through thecollaborative workspace (e.g., a GUI representation thereof).

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Additionalaspects, features, and/or advantages of examples will be set forth inpart in the description which follows and, in part, will be apparentfrom the description, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive examples are described with reference tothe following figures.

FIG. 1 illustrates an exemplary system diagram of components interfacingto enable automatic generation and management of collaborative mediaobjects and data insights within an exemplary collaborative workspace,with which aspects of the present disclosure may be practiced.

FIG. 2A illustrates exemplary method related to automatic generation andmanagement of exemplary collaborative media objects, with which aspectsof the present disclosure may be practiced.

FIG. 2B illustrates exemplary method related to automatic generation andmanagement of data insights to aid user collaboration within acollaborative workspace, with which aspects of the present disclosuremay be practiced.

FIGS. 3A-3L illustrate exemplary processing device views associated withuser interface examples for an improved user interface that is adaptedfor generation and management of collaborative media objects, with whichaspects of the present disclosure may be practiced.

FIGS. 4A-4J illustrate exemplary processing device views associated withuser interface examples for an improved user interface that is adaptedfor generation and management of data insights to aid user collaborationwithin a collaborative workspace, with which aspects of the presentdisclosure may be practiced.

FIG. 5 illustrates a computing system suitable for implementingprocessing operations described herein related to generation andmanagement of collaborative media objects and data insights within anexemplary collaborative workspace, with which aspects of the presentdisclosure may be practiced.

DETAILED DESCRIPTION

As identified in the foregoing, there is a technical need for acollaborative workspace that fosters content creation between users in asynchronous and/or asynchronous manner. An exemplary collaborativeworkspace, presentable through a GUI of an application or service, isadapted to enable automatic generation and management of collaborativemedia objects. Collaborative media objects are generated thatautomatically combine content from a plurality of users into a singlemedia object. This is extremely beneficial in technical scenarios whereusers are creating projects, assignments, presentations, etc., byremoving the need for users to manually stitch together and combinecontent to create a final product. In some technical instances, thecollaborative workspace is adapted for a video discussionapplication/service, where users create one or more video clips (e.g.,video feeds, live camera feeds) in response to a posted topic. In atleast one example, a collaborative workspace for a video discussionapplication/service may be integrated to display within another type ofapplication/service to further improve processing efficiency and userproductivity. However, it is to be understood that processing describedin the present disclosure is extensible to work with any type ofapplication/service and any content type.

For ease of understanding, a non-limiting example of a video discussionapplication/service is Flipgrid® video discussion application/service.The present disclosure describes both back-end processing (e.g.,server-side processing) and front-end representations (e.g., an adaptedGUI) that bring online collaboration to the Flipgrid® cameraapplication/service. Through the present disclosure, users are now ableto join the same video creation session which is adapted to fostercollaboration allowing multiple users to join and record video clips inresponse to a posted topic. In some examples, video clips can berecorded at the same time where the collaborative workspace isconfigured to intelligently (and automatically) create an arrangement ofthose video clips as a single media object. For instance, a teacher maypost an assignment for students via the Flipgrid® camera, where a groupof students can collaboratively create and/or upload video clips tocreate a response to the assignment for submission and subsequent reviewby the teacher. Video clips recorded and/or uploaded by users areautomatically combined to create a combined work product (e.g.,collaborative media object) for posting and/or submission. Acollaborative media object can further be collaboratively edited by agroup of users in association with a collaborative session of thecollaborative workspace. Collaborative sessions described herein maypertain to synchronous and/or asynchronous user collaboration through anexemplary collaborative workspace and/or other application/serviceendpoints.

Furthering aspects of collaborative integration, users can return to acollaborative workspace at a later point in time and modify acollaborative media object (e.g., add/delete video clips, trim,rearrange, add dueting). For example, recall processing may occurthrough a collaborative workspace and/or through a differentapplication/service endpoint. Changes made to a collaborative mediaobject are automatically updated (e.g., through a collaborativeworkspace) for all users who are participating in an ongoingcollaborative session via a collaborative workspace. For instance,collaboration is extremely useful in educational scenarios wherestudents may collaborate remotely from different locations. An exemplarycollaborative workspace allows students to start a collaborativesession, record video clips and then wait for another student (e.g., ina different time zone) to record their own clip to add to an assignmentwhile treating the entire user experience as a single session with asingle work product.

As a non-limiting example, users may access a collaborative workspacethrough a video discussion application/service. That is, a collaborativeworkspace is tailored for presentation via a video discussionapplication/service. An exemplary collaborative workspace fosterssynchronous and/or asynchronous user collaboration via a group spacethat is accessible to multiple users. In some examples, collaboration,through the collaborative workspace, occurs where two or more users whoconcurrently access the collaborative workspace. In alternativeexamples, users may utilize the collaborative workspace in anasynchronous manner to conduct user collaboration. For example, thecollaborative workspace is a real-time (or near real-time) electroniccollaboration session that is concurrently accessed by at least twousers and further provides a topic for the at least two users to respondto by providing video feeds. Video feeds can be recorded live throughthe collaborative workspace (e.g., during a collaborative session)and/or uploaded by users within the collaborative session. Thecollaborative workspace is configured to detect and analyze addedcontent and automatically generate a single media object (e.g.,collaborative media object) from one or more added video feeds. Portionsof the single media object can be edited (e.g., within the collaborativeworkspace) but the entirety of the collaborative media object is atreated a single media object. This is beneficial for not only review ofa submission of the collaborative media object (e.g., by a teacher) butalso for users who want to export or transfer the collaborative mediaobject to work in other applications/services.

Furthering the above discussion, a technical scenario may be consideredin which multiple video feeds are added to a collaborative workspace.For instance, a component of a video discussion application/service mayreceive a first live video feed from a first device associated with afirst user of a collaborative session. The first live video feed isrecorded within the collaborative workspace in response to a postedtopic associated with the collaborative workspace. Additionally, asecond live video feed may be added from a second device associated witha second user of a collaborative session. Similarly, the second livevideo feed may be recorded within the collaborative workspace inresponse to the posted topic associated with the collaborativeworkspace. In further examples, a third video feed, that was previouslyrecorded by a user, may be uploaded to the collaborative workspace aspart of an assignment submission. As an example, the third video feedmay be uploaded via a third device associated with a third user of thecollaborative session. However, any user may record or upload content.Regardless of the number of video feeds and/or video clips that areadded to the collaborative workspace, the collaborative workspace maygenerate a collaborative media object that automatically combines thevideo feeds/clips (and in some cases content portions of other contenttypes) into a single media object for presentation in the collaborativeworkspace of the video discussion application or service. For example,generation of the collaborative media object comprises aggregating aclip of the first live video feed (or live camera feed) and a clip ofthe second live video feed (or live camera feed) in a sequential order(and the third video feed in such examples) to create the single mediaobject. In instances where additional feeds are added (e.g., third videofeed), the collaborative media object may further incorporate additionalvideo feeds into a single media object. In some technical examples,generation of the collaborative media object may comprise execution oftrained artificial intelligence (AI) processing that executes processingoperations to intelligently determine how to arrange the content (e.g.,clips of video feeds) for aggregation. For instance, an AI model (ormodels) may be trained to evaluate the importance of video clips to aposted topic (and/or relevance between added clips) to determine how toorder or present an arrangement of video clips as a single media object.Any types of signal data, alone or in combination, may be utilized toaid importance/relevance ranking processing to generate determinationsincluding but not limited to automatic determinations with respect to:editing capabilities of users (e.g., user settings for collaborativeediting/viewing); generation of presence indications for interactionswith a collaborative media object during one or more collaborativesessions; generation of a dynamic timeline providing temporalrepresentation of user interactions with a collaborative media object;and generation and provision of data insight representations for acollaborative workspace (e.g., for interaction with a collaborativemedia object), among other examples.

Continuing the above example, data for rendering of the collaborativemedia object may be transmitted for display in a GUI representation ofthe collaborative workspace. For instance, this may occur in distributedexamples where a component is executing processing and transmits, over anetwork connection, data for rendering a representation of a GUI on aclient computing device (e.g., user computing device). As an example,transmission of data for rendering a collaborative media object, andrepresentations thereof, may comprise transmitting, to a client device,data for rendering the collaborative media object in a GUI presentingthe collaborative workspace (e.g., within the video discussionapplication/service). In other examples, processing to generate acollaborative media object, and representations thereof, may occurdirectly on a client device that is rendering a user representation ofthe collaborative workspace (e.g., representation of collaborativeworkspace for first user of a group of users).

Further examples described herein pertain to generation of notificationrepresentations of activity for users, which may be extremely beneficialto keep users in the loop while other users are collaboratively workingon the same collaborative media object. For example, a collaborativemedia management component is configured to detect presence data ofusers during interaction with a collaborative media object (or portionsthereof). Presence indications may be generated and rendered in a GUIrepresentation of the collaborative workspace that identify useractivity (e.g., past actions, present activity and/or future intendedactivity). In distributed processing examples, data for renderingpresence indications may be transmitted, to a client computing device,for rendering/display in a GUI (e.g., of a video discussionapplication/service).

In additional examples, activity notifications may be generated andpresented for users which comprise a dynamic timeline providing temporalrepresentation of user interactions with a collaborative media object.For instance, since collaborative editing may occur in real-time, oneuser may add a video clip and then realize that another user may havemodified that video clip. As such, a collaborative media managementcomponent may be configured to generate dynamic media managementtimelines identifying user interactions (e.g., modification) with acollaborative media object as well as identification of a timing of whenan interaction occurred. This can aid in providing users with a fullerpicture of a collaborative session and even help identify a point thatan edit should be rolled back or returned to a previous version of thecollaborative media object.

To improve processing efficiency and user productivity relative to acollaborative workspace, the present disclosure further providesprocessing that applies trained artificial intelligence (AI) processingthat is adapted to automatically generate representations of datainsights for management of a collaborative media object within thecollaborative workspace. Data insights may be generated pertaining tofeature management of an exemplary application/service (e.g., videodiscussion application/service) including features for generating andmanaging a collaborative media object. Signal data associated with usersof a collaborative session (e.g., within a collaborative workspace) maybe detected and analyzed. While a plurality of different types of signaldata are applicably described herein, detected signal data comprisesapplication-specific signal data pertaining to user interactions ofusers within a collaborative workspace (e.g., of a video discussionapplication or service). Analysis of signal data leads to adetermination as to a context of users within a collaborative workspace.Contextually relevant data insights may then be generated, whererepresentations of data insights are provided for rendering through thecollaborative workspace (e.g., a GUI representation thereof).

In generating contextually relevant data insights, trained AI processingis configured to execute a relevance evaluation (e.g., relevancedeterminations) that identifies relevant features of a video discussionapplication/service to determine user context of one or more userswithin the collaborative workspace. In some examples, a user contextevaluation is generated collectively for user collaboration of two ormore users within the collaborative workspace. This type of evaluationmay more accurately capture a state of user collaboration within acollaborative workspace. With respect to a relevance evaluation, atrained AI model may be trained to generate relevance scoring (e.g.,relevance ranking processing) that scores the relevance of individualdata insights to a user context and/or a state of a collaborative mediaobject. As a non-limiting example, relevance scoring for a data insightmay be derived from analysis of collected signal data, an identifieduser context (individually and/or collaboratively), a state of acollaborative media object within the collaborative workspace or anycombination thereof. For instance, user context within the collaborativeworkspace may be comparatively evaluated relative to states ofcollaborative media object (e.g., a current state of the collaborativemedia object as well as future state that is intended to be reached as afinal product that ultimately contemplates the parameters set forth forcontent submission). As a collaborative workspace is expansive and auser context can vary from collaborative session to collaborativesession, non-limiting examples of specific types of relevanceevaluations (and associated scoring metrics) are subsequently disclosedherein. It is noted that a trained AI model may be configured to focuson a specific relevance scoring metric or may be trained to collectivelyconsider multiple relevance scoring metrics to determine contextuallyrelevant data insights for presentation.

In some examples, representations of data insights may be generated inreal-time (or near real-time) depending on a user context relative tothe collaborative workspace. In other examples, representations of datainsights may be generated asynchronously from user access and thensurfaced at the most relevant timing based on analysis of signal datareceived relative to the collaborative workspace. Representations ofdata insights may comprise not only content defining a contextuallyrelevant data insight but also GUI elements linked to features of avideo discussion application/service that are provided through acollaborative workspace. For instance, a representation of a datainsight may comprise a selectable GUI element, that when selected,provides automatic execution of a feature of the video discussionapplication or service. As an example, multiple users may becollaboratively creating a collaborative media object, where selectableGUI elements may be presented to help users create (or add content to)the collaborative media object and/or manage editing of the same. Infurther examples, representations of data insights may enable automatedimport/export control of a collaborative media object across differentapplications/services. In even further examples, representations of datainsights may further enable users to integrate a GUI version of thecollaborative workspace within a GUI of another application/service.This is extremely beneficial to users who are multi-tasking or aresimply accessing content primarily in another application/service.

Exemplary technical advantages provided by processing described in thepresent disclosure comprise but are not limited to: improved server-sideprocessing for management of a collaborative workspace within differenttypes of applications/services (e.g., a video discussionapplication/service); processing operations to more intelligentlygenerate and manage exemplary collaborative media objects presentedwithin a collaborative workspace or other application/service endpoint;generation of GUI representations of collaborative media objectsincluding automatic rendering and presentation of GUI features (e.g.,notifications/menus) that present collaborative media objects andmanagement of user interactions therewith; generation of GUIrepresentations of data insights for a collaborative workspace automaticrendering and presentation of GUI features (e.g., notifications/menus)that present representations of data insights and management of userinteractions therewith; application of specific purpose computing togenerate collaborative media objects and representation of data insightsincluding, in some technical examples, application of trained AIprocessing to aid content generation and provision; improved processingefficiency (e.g., reduction in processing cycles, savingresources/bandwidth) for computing devices when generating and renderinga collaborative workspace including generation and presentation ofexemplary collaborative media objects; improved processing efficiency(e.g., reduction in processing cycles, saving resources/bandwidth) forcomputing devices when generating data insights and representationsthereof, adapting and improving a GUI of an application/service tointegrate GUI elements for the provision and management of collaborativemedia objects and representations of data insights for a collaborativeworkspace; reduction in latency through efficient processing operationsthat improve collaboration via specific types of applications/services;extensibility to customize representations of collaborative workspacesfor user-specific presentation; implementation of a novel collaborativemedia management component that is further configured to interface witha plurality of applications/services (e.g., applications/services of adistributed software platform) to extend functionality during processingdescribed herein; and improving usability of applications/services forusers via integration of processing described herein, among othertechnical advantages.

FIG. 1 illustrates an exemplary system diagram 100 of componentsinterfacing to enable automatic generation and management ofcollaborative media objects and data insights within an exemplarycollaborative workspace, with which aspects of the present disclosuremay be practiced. System diagram 100 describes components that may beutilized to execute processing operations described in method 200 (FIG.2A) and method 250 (FIG. 2B) as well as processing described in andassociated with visual diagrams of FIGS. 3A-3L, FIGS. 4A-4J and therespective accompanying descriptions. Moreover, interactions betweencomponents of system diagram 100 may be altered without departing fromthe spirit of the present disclosure. Exemplary components, described insystem diagram 100, may be hardware and/or software components, whichare programmed to execute processing operations described herein. Insome examples, components of system diagram 100 may each be one or morecomputing devices associated with execution of a specific service.Exemplary services may be managed by a software data platform (e.g.,distributed software platform) that also provides, to a component,access to and knowledge of other components that are associated withapplications/services. In one instance, processing operations describedin system diagram 100 may be implemented by one or more componentsconnected over a distributed network, where a user account may beworking with a specific profile established through a distributedsoftware platform. System diagram 100 comprises user computing devices102; an application/service component 104; a collaborative mediamanagement component 106; a component for implementation trained AIprocessing 108; and knowledge repositories 110.

System diagram 100 comprises user computing device(s) 102 (e.g., clientcomputing device). An example of a user computing device 102 is acomputing system (or computing systems) as described in the descriptionof FIG. 5. A user may interact with an exemplary application/service,provided by an application/service component 104, through the usercomputing device(s) 102. For instance, the user may connect to anapplication/service through any number of different device modalities.Examples of modalities and communication via the same are known to oneskilled in the field of art. Non-limiting examples of different types ofmodalities comprise but are not limited to: collaborative sessions,chat, user feeds (e.g., live feeds including speech input and/or videofeeds); messaging (including SMS and instant messaging); email;collaborative communication channels; collaborative electronicdocuments; VR/AR workspaces, remote calls (e.g., VOIP); remote meetings(e.g., electronic meetings), or a combination thereof, among otherexamples. In further examples, a user may carry on a multi-modalcommunication with an application/service via multiple user computingdevices, where the present disclosure is intended to cover suchtechnical instances. Collaborative sessions described herein may pertainto synchronous and/or asynchronous user collaboration through anexemplary collaborative workspace and/or other application/serviceendpoints.

In some examples, a user computing device 102 may connect to anapplication/service (e.g., a productivity application/service) that isspecifically a video discussion application/service. For ease ofunderstanding, a non-limiting example of a video discussionapplication/service is Flipgrid® where back-end processing (e.g.,server-side processing) behind a video discussion application andfront-end representations (e.g., an adapted GUI) bring onlinecollaboration to the Flipgrid® camera application/service. A videodiscussion application/service is configured to enable users to conductcommunication through the posting (or submission) of video clips. Videoclips (or clips) may comprise but are not limited to: live video feedsof one or more users; camera feeds (e.g., that include previouslyrecorded content), and the like as known to one skilled in the field ofart. A live camera feed as described herein is intended to cover livestreaming instances where video data, among other types of data (e.g.,audio), is processed as a data stream (e.g., video stream). Processingfor rendering and presenting live streams and types of data streams,including representations of multiple data streams for creation of livefeeds, are known to one skilled in the field of art. For instance, alive video feed may comprise a plurality of data streams including butnot limited to a video stream and an audio stream. Users of a videodiscussion application/service may post videos, video clips, feeds,etc., in response to a topic that is posted for discussion. Forinstance, a teacher may post a topic for students to respond to for aneducational assignment. For ease of understanding, a non-limitingexample is described in the present disclosure that pertains to ateacher posting a topic for a film study class (“Film Study 101”), wherea group of students are posting videos in response to a postedassignment. In traditional implementations of video discussionapplications/services, users (students) would be required to manuallystitch together multiple video clips to create a response to anassignment. Above that, posted video clips have been traditionallytreated as their own objects rather than a combined data object. Thisposes technical challenges when users are collaboratively editingcontent and manually attempting to combine content into a final productfor assignment submission.

For a high-level implementation understanding, Flipgrid® is a webapplication/service (e.g., providing a website) that allows users (e.g.)teachers to create “groups” which may then be used to facilitate videodiscussions. Each group acts like a message board where a user (e.g., ateacher) can pose questions (e.g., called “topics”) for replies, andother users (e.g., their students) can post video responses that appearin a tiled grid display. Exemplary groups can be shared with classes,small groups, or any collection of users interested in a similar topicor strand of questions. For extensibility, each group can hold anunlimited number of topics and each topic can hold an unlimited numberof responses. Topics may be text-based or include a resource such as animage, video, Giphy, emoji, attachment, or the like. Users (e.g.,students) can respond via the Flipgrid® application or website with anycamera-enabled device or by uploading a previously recorded video. Thelength of a response video may vary and may further be preset byadministrative users (e.g., teachers or other posters of content). Users(e.g., teachers) can also allow students to record replies to other userposts (e.g., classmates' responses). Within an exemplary videodiscussion application/service, numerous features are available to usersto aid with video recording and editing. Non-limiting examples of suchfeatures comprise but are not limited to: camera options to managecontent addition (e.g., recording of a video clip, uploading of a videoclip, mirroring of video, audio control, video control, recording of ascreen, image snapshots); camera effect options (e.g., adding offilters, frames, emojis, text, drawings, addition of boards such aswhiteboards, resizing and cropping); and features for topic managementand/or note addition (e.g., the addition of sticky notes that aredisplayed for users within a collaboration workspace). Further, in thepresent disclosure, a new effect feature is also introduced allowingusers to create a dueting of a video clip. Dueting enables users torecord a video clip (or concurrently play a pre-recorded video clip)simultaneously with the playback of another video clip. Essentially,users can build off another user's video by recording their own video(s)alongside another video as it plays, thereby providing a new layer ofcreativity and user interaction. Notably, an improved GUI is adapted toenable users to collaboratively create a dueting clip (e.g., multipleusers can create a duet with a video engaged in playback), including GUIfeatures that enable automatic initiation of a collaborative duet andcontrol over which users are involved in the dueting clip.

An exemplary application/service component 104 is one or more componentsconfigured to provide access to data associated with an application orservice and further provide renderings of GUIs of applications/servicesthat are accessible by the user computing device 102.Applications/services, provided through the application/servicecomponent 104, may be any type of programmed software. An exemplaryapplication/service is a productivity application/service that isconfigured for execution of tasks including collaborative communicationbetween users (e.g., via a collaborative workspace) where multiple usersare engaged synchronously and/or asynchronously in a collaborativesession. As referenced in the foregoing description, a non-limitingexample of an application/service (e.g., productivityapplication/service) is a video discussion application or service. Forinstance, one or more users may utilize the collaborative workspace tocreate a collaborative media object providing a plurality of videoclips, collectively assembled, in response to a posted topic. However,it is to be understood that processing described in the presentdisclosure is extensible to work with any type of application/serviceand any content type. Non-limiting examples of productivity applicationsor services that are applicable in the present disclosure comprise butare not limited to: video discussion applications/services; wordprocessing applications/services; spreadsheet applications/services;notes/notetaking applications/services; authoring applications/services;digital presentation applications/services; presentation broadcastingapplications/services; search engine applications/services; emailapplications/services; messaging applications/services; web browsingapplications/services; collaborative communicationapplications/services; digital assistant applications/services; webpagebuilding applications/service; directory applications/services; mappingservices; calendaring services; electronic payment services; digitaldata storage or distributed data storage applications/services; webconferencing applications/services; call communicationapplications/services; language understanding applications/services; botframework applications/services; networking applications/service; andsocial networking applications/services, among other examples.

In at least one example, examples described herein extend to integratingpresentation of a collaborative workspace of a first application/service(e.g., a video discussion application/service) within a secondapplication/service that is different from the firstapplication/service. For instance, a user may have a plurality ofapplications/services open but be focused on a specificapplication/service (e.g., user is part of an electronic meeting), whereit is inefficient to continuously switch between applications/serviceswhile focused on a specific task. The present disclosure bringsfunctionality and extensibility to user collaboration, where a GUIdisplay/rendering of a collaborative workspace can be integrated intoanother application/service endpoint (e.g., GUI of anapplication/service). In some examples, an exemplary productivityapplication/service may be a component of a distributed softwareplatform providing a suite of productivity applications/services. Adistributed software platform is configured to providing access to aplurality of applications/services, thereby enablingcross-application/service usage to enhance functionality of a specificapplication/service at run-time. Distributed software platforms mayfurther manage tenant configurations/user accounts to manage access tofeatures, applications/services, etc. as well access to distributed datastorage (including user-specific distributed data storage). Moreover,specific application/services (including those of a distributed softwareplatform) may be configured to interface with other non-proprietaryapplication/services (e.g., third-party applications/services) to extendfunctionality including data transformation and associatedimplementation.

Exemplary applications/services, provided by the application/servicecomponent 104, may interface with other components of system diagram 100to enhance processing efficiency and functionality as described herein.For instance, the application/service component 104 is configured tointerface with a user computing device 102 as well as the collaborativemedia management component 106, component for implementation trained AIprocessing 108 and knowledge repositories 110 (e.g., of a distributedsoftware platform). In doing so, signal data may be collected andanalyzed one or more of: the application/service component 104; thecollaborative media management component 106, component forimplementation trained AI processing 108 and, knowledge repositories110, to enable contextual processing of data pertaining to acollaborative workspace (e.g., of a video discussionapplication/service). Signal data may be collectively analyzed togeneration determinations described herein including those where thecollaborative media management component 106 and/or componentimplementing trained AI processing 108 are generating and applyingimportance/relevance scoring/ranking to automatically generatedeterminations described herein. For instance, application of trained AImodel (or models) may be trained to evaluate the importance of videoclips to a posted topic (and/or relevance between added clips) todetermine how to order or present an arrangement of video clips as asingle media object. This type of importance/relevance processing mayfurther be utilized to execute other types of processing determinationsincluding but not limited to: editing capabilities of users (e.g., usersettings for collaborative editing/viewing); generation of presenceindications for interactions with a collaborative media object duringone or more collaborative sessions; generation of a dynamic timelineproviding temporal representation of user interactions with acollaborative media object; and generation and provision of data insightrepresentations for a collaborative workspace (e.g., for interactionwith a collaborative media object), among other examples. Non-limitingexamples of signal data that may be collected and analyzed comprises butis not limited to: device-specific signal data collected from operationof one or more user computing devices 102; user-specific signal datacollected from specific tenants/user-accounts with respect to access toany of: devices, login to a distributed software platform,applications/services, etc.; and application-specific data collectedfrom usage of applications/services (e.g., via a collaborative workspaceand/or other application/service endpoints). In further examples,analysis of signal data may comprise identifying correlations andrelationships between the different types of signal data, wheretelemetric analysis may be applied to generate determinations withrespect to a contextual state of a collaborative workspace. Forinstance, analysis of device-specific signal data, user-specific signaldata and application-specific data can be collectively analyzed todetermine how to automatically arrange content portions of acollaborative media object as well as generate GUI notifications anddata insights associated with a collaborative workspace. Analysis ofsuch types of signal data in an aggregate manner may be useful inhelping generate contextually relevant data objects, notifications, datainsights and representations thereof, etc. In some examples, signal datacollected and analyzed may comprise data indicating user interactionhistory (or past user preferences), for example, while working in acollaborative workspace and/or specific application/service (e.g., videodiscussion application/service). This type of relevance analysis may behelpful to generate data insights that are most contextually relevant toa user (or group of users).

With respect to application-specific signal data, signal data indicatinguser interactions of users within a collaborative workspace (e.g., of avideo discussion application or service) may be collected and analyzedto determine context of users within a collaborative workspace. In someexamples, trained AI processing may be trained to focus on specifictypes of user interactions such as those pertaining to generation andmanagement of a collaborative media object. This may lead to generationof data insights that further the creation of a collaborative mediaobject as well as those that bring attention to features of a videodiscussion application/service that further collaborative media objectcreation. In some examples, a user context evaluation is generatedcollectively for user collaboration of two or more users within thecollaborative workspace. This type of evaluation may more accuratelycapture a state of user collaboration within a collaborative workspace.However, in further examples, signal data pertaining to a userassociated with a collaborative workspace may comprise signal datathrough computing devices and/or other applications/services, which canhelp provide a clearer picture of a context of a user relative to actionwithin the collaborative workspace.

With respect to a relevance evaluation, a trained AI model may betrained to analyze signal data, determine user context within acollaborative workspace and generate relevance scoring (e.g., relevanceranking processing) that scores the relevance of individual datainsights to a user context and/or a state of a collaborative mediaobject within a collaborative workspace. As a non-limiting example,relevance scoring for a data insight may be derived from analysis ofcollected signal data, an identified user context (individually and/orcollaboratively), a state of a collaborative media object within thecollaborative workspace, or any combination thereof. For instance, usercontext within the collaborative workspace may be comparativelyevaluated relative to states of collaborative media object (e.g., acurrent state of the collaborative media object as well as future statethat is intended to be reached as a final product that ultimatelycontemplates the parameters set forth for content submission). It isnoted that a trained AI model may be configured to focus on a specificrelevance scoring metric or may be trained to collectively considermultiple relevance scoring metrics to determine contextually relevantdata insights for presentation. In some examples, developers may set aweighting to specific types of signal data (e.g., those pertaining tointeractions with a collaborative media object) to foster generation ofcontextually relevant suggestions for an objective task within anapplication/service.

The application/service component 104 is further configured to present,through interfacing with the collaborative media management component106, an adapted GUI that provides user notifications, GUI menus, GUIelements, etc., to manage collaborative sessions presented through acollaborative workspace. For instance, a GUI of an application/service(e.g., video discussion application/service) may be configured toprovide user interface elements that display an active state of acollaborative media object as it is edited throughout a collaborativesession. This may include automatic generation and rendering of GUIfeatures/elements that are presented without a user having to takemanual action to aggregate video clips into a single data object. Inother instances, an application command control (e.g., user interfaceribbon and/or GUI menus) may be adapted to include selectable userinterface features to manage states of representations of collaborativemedia objects. Non-limiting visual examples of an improved GUI, and GUIelements provided therein, are provided in FIGS. 3A-3L and FIGS. 4A-4J.

The collaborative media management component 106 is one or morecomponents configured to execute and manage processing operationsrelated to generation and provision of a collaborative workspace,collaborative media objects and representations of data insights for acollaborative workspace as described herein. In some examples, thecollaborative media management component 106 may be a distributedcomputing device (e.g., distributed server device) that executesprocessing asynchronously from the user computing device 102 which isusable to access a GUI of an application/service. In other examples, thecollaborative media management component 106 may be configured as acomponent that executes on the user computing device 102. In alternativeexamples, the collaborative media management component 106 is a systemof components that execute across one or more server devices and one ormore components executing on the user computing device 102, where anetwork connection is usable to connect the components in a systemconfiguration. The collaborative media management component 106 may beconfigured to execute any processing operations described herein,including those described relative to method 200 (FIG. 2A), method 250(FIG. 2B) and processing associated with visual diagrams of FIGS. 3A-3L,FIGS. 4A-4J and further described in the respective accompanyingdescriptions. It is further to be recognized that an order of executionof processing operations by the collaborative media management component106 may vary without departing from the spirit of the presentdisclosure.

To improve processing efficiency and user productivity relative to acollaborative workspace, the collaborative media management component106 is configured to interface with a component for implementation oftrained AI processing 108 to apply trained AI processing that is adaptedto automatically generate representations of data insights formanagement of a collaborative media object within the collaborativeworkspace. Application of trained AI modeling enables generation ofcontextually relevant data insights pertaining to feature management ofan exemplary application/service (e.g., video discussionapplication/service) including features for generating and managing acollaborative media object. Signal data associated with users of acollaborative session (e.g., within a collaborative workspace) may bedetected and analyzed. While a plurality of different types of signaldata are applicably described herein, detected signal data comprisesapplication-specific signal data pertaining to user interactions ofusers within a collaborative workspace (e.g., of a video discussionapplication or service). Analysis of signal data leads to adetermination as to a context of users within a collaborative workspace(individually or collaboratively). Contextually relevant data insightsmay then be generated, where representations of data insights areprovided for rendering through the collaborative workspace (e.g., a GUIrepresentation thereof).

In generating contextually relevant data insights, trained AIprocessing, directed by the collaborative media management component106, is configured to execute a relevance evaluation (e.g., relevancedeterminations) that identifies relevant features of a video discussionapplication/service to user context of one or more users within thecollaborative workspace. In some examples, a user context evaluation isgenerated collectively for user collaboration of two or more userswithin the collaborative workspace. This type of evaluation may moreaccurately capture a state of user collaboration within a collaborativeworkspace. With respect to a relevance evaluation, a trained AI modelmay be trained to generate relevance scoring (e.g., relevance rankingprocessing) that scores the relevance of individual data insights to auser context and/or a state of a collaborative media object. As anon-limiting example, relevance scoring for a data insight may bederived from analysis of collected signal data, an identified usercontext (individually and/or collaboratively), a state of acollaborative media object within the collaborative workspace, or anycombination thereof. For instance, user context within the collaborativeworkspace may be comparatively evaluated relative to states ofcollaborative media object (e.g., a current state of the collaborativemedia object as well as future state that is intended to be reached as afinal product that ultimately contemplates the parameters set forth forcontent submission). As a collaborative workspace is expansive and auser context can vary from collaborative session to collaborativesession, non-limiting examples of specific types of relevanceevaluations (and associated scoring metrics) are subsequently disclosedherein. It is noted that a trained AI model may be configured to focuson a specific relevance scoring metric or may be trained to collectivelyconsider multiple relevance scoring metrics to determine contextuallyrelevant data insights for presentation.

In some examples, representations of data insights may be generated inreal-time (or near real-time) depending on a user context relative tothe collaborative workspace. In other examples, representations of datainsights may be generated asynchronously from user access and thensurfaced at the most relevant timing based on analysis of signal datareceived relative to the collaborative workspace. Representations ofdata insights may comprise not only content defining a contextuallyrelevant data insight but also GUI elements linked to features of avideo discussion application/service that are provided through acollaborative workspace. For instance, a representation of a datainsight may comprise a selectable GUI element, that when selected,provides automatic execution of a feature of the video discussionapplication or service. As an example, multiple users may becollaboratively creating a collaborative media object, where selectableGUI elements may be presented to help users create (or add content to)the collaborative media object and/or manage editing of the same. Infurther examples, representations of data insights may enable automatedimport/export control of a collaborative media object across differentapplications/services. In even further examples, representations of datainsights may further enable users to integrate a GUI version of thecollaborative workspace within a GUI of another application/service.This is extremely beneficial to users who are multi-tasking or aresimply accessing content primarily in another application/service.

Moreover, a component for implementation trained AI processing 108 maybe applied to aid generation of processing determinations of thecollaborative media management component 106. An exemplary component forimplementation trained AI processing 108 may manage AI modelingincluding the creation, training, application, and updating of AImodeling. In cases where trained AI processing is applied, generalapplication of trained AI processing including creation, training andupdate thereof is known to one skilled the field of art. Above what istraditionally known, trained AI processing may be adapted to executespecific determinations described herein with reference to thecollaborative media management component 106 and processing operationsexecuted thereby. For instance, AI model may be specifically trained andadapted for execution of processing operations comprising but notlimited to: generation of collaborative media objects includingarrangement of content thereof; editing capabilities of users (e.g.,user settings for collaborative editing/viewing); generation of presenceindications for interactions with a collaborative media object duringone or more collaborative sessions; generation of a dynamic timelineproviding temporal representation of user interactions with acollaborative media object; and generation and provision of data insightrepresentations for a collaborative workspace (e.g., for interactionwith a collaborative media object), among other examples. Exemplary AIprocessing may be applicable to aid any type of determinative orpredictive processing by the collaborative media management component106, via any of: supervised learning; unsupervised learning;semi-supervised learning; or reinforcement learning, among otherexamples. Non-limiting examples of supervised learning that may beapplied comprise but are not limited to: nearest neighbor processing;naive bayes classification processing; decision trees; linearregression; support vector machines (SVM) neural networks (e.g.,convolutional neural network (CNN) or recurrent neural network (RNN));and transformers, among other examples. Non-limiting of unsupervisedlearning that may be applied comprise but are not limited to:application of clustering processing including k-means for clusteringproblems, hierarchical clustering, mixture modeling, etc.; applicationof association rule learning; application of latent variable modeling;anomaly detection; and neural network processing, among other examples.Non-limiting of semi-supervised learning that may be applied comprisebut are not limited to: assumption determination processing; generativemodeling; low-density separation processing and graph-based methodprocessing, among other examples. Non-limiting of reinforcement learningthat may be applied comprise but are not limited to: value-basedprocessing; policy-based processing; and model-based processing, amongother examples. Furthermore, a component for implementation of trainedAI processing 108 may be configured to apply a ranker to generaterelevance scoring to assist with any processing determinations by thecontextual insight generation component 106. Non-limiting examples ofrelevance scoring, and specific metrics used for relevance scoring, aresubsequently described, including the description of method 200 (FIG.2A) and method 250 (FIG. 2B). Scoring for relevance (or importance)ranking may be based on individual relevance scoring metrics describedherein or an aggregation of said scoring metrics. In some alternativeexamples where multiple relevance scoring metrics are utilized, aweighting may be applied that prioritizes one relevance scoring metricover another depending on the signal data collected and the specificdetermination being generated for the collaborative media managementcomponent 106.

As referenced in the foregoing description, knowledge repositories 110may be accessed to obtain data for generation, training andimplementation of the component for implementation of trained AIprocessing 108 as well the operation of processing operations by that ofthe application/service component 104 and the collaborative mediamanagement component 106. Knowledge resources comprise any dataaffiliated with a software application platform (e.g., Microsoft®,Google®, Apple®, IBM®) as well as data that is obtained throughinterfacing with resources over a network connection includingthird-party applications/services. Knowledge repositories 110 may beresources accessible in a distributed manner via network connection thatmay store data usable to improve processing operations executed by thecollaborative media management component 106. Examples of datamaintained by knowledge repositories 110 comprises but is not limitedto: collected signal data (e.g., from usage of an application/service,device-specific, user-specific); telemetry data including past usage ofa specific user and/or group of users; corpuses of annotated data usedto build and train AI processing classifiers for trained AI modeling;access to entity databases and/or other network graph databases usablefor evaluation of signal data; web-based resources including any dataaccessible via network connection including data stored via distributeddata storage; trained bots including those for natural languageunderstanding; software modules and algorithms for contextual evaluationof content and metadata; and application/service data (e.g., data ofapplications/services managed by the application/service component 104)for execution of specific applications/services including electronicdocument metadata, among other examples. In even further examples,telemetry data may be collected, aggregated, and correlated (e.g., by aninterfacing application/service) to further provide the collaborativemedia management component 106 with on demand access to telemetry datawhich can aid determinations generated thereby. Furthermore, knowledgerepositories 110 may be utilized to manage storage of generated datainsights (and representations thereof). In some examples, data insightsand/or representations thereof may be generated in real-time (or nearreal-time) during access to a collaborative workspace by users. In otherexamples, data insights and/or representations thereof may be generatedasynchronously from user access to a collaborative workspace. Forinstance, after users temporarily exit a collaborative session,processing to evaluate user interactions within a collaborativeworkspace may occur, where data insights may be generated for laterrepresentation (e.g., surfacing to users upon subsequent access to thecollaborative workspace). In any example, data insights (andrepresentations thereof) may be stored for recall via theapplication/service component 104. For instance, a repository of datainsights and/or representations of data insights may be stored on adistributed data storage for on-demand access.

FIG. 2A illustrates exemplary method 200 related to automatic generationand management of exemplary collaborative media objects, with whichaspects of the present disclosure may be practiced. As an example,method 200 may be executed across an exemplary computing system 501 (orcomputing systems) as described in the description of FIG. 5. Exemplarycomponents, described in method 200, may be hardware and/or softwarecomponents, which are programmed to execute processing operationsdescribed herein. Non-limiting examples of components for operations ofprocessing operations in method 200 are described in system diagram 100.Processing operations performed in method 200 may correspond tooperations executed by a system and/or service that execute computermodules/programs, software agents, application programming interfaces(APIs), plugins, AI processing including application of trained datamodels, intelligent bots, neural networks, transformers and/or othertypes of machine-learning processing, among other examples. In onenon-limiting example, processing operations described in method 200 maybe executed by a component such as a collaborative media managementcomponent 106 (of FIG. 1) and/or a component for implementation oftrained AI processing 108. In distributed examples, processingoperations described in method 200 may be implemented by one or morecomponents connected over a distributed network. For example, componentsmay be executed on one or more network-enabled computing devices,connected over a distributed network, that enable access to usercommunications.

Method 200 begins at processing operation 202, where data is providedfor rendering of a GUI representation of a collaborative workspace of avideo discussion application/service. While examples of method 200 mayreference an application/service as a video discussion application orservice, it is to be understood that processing operations described inmethod 200 are applicable to work with any type of application orservice described herein including those specifically described withrespect to description of the application/service component 104. Asreferenced in the foregoing, a video discussion application/service isconfigured to enable users to conduct communication through the posting(or submission) of video clips. Video clips (or clips) may comprise butare not limited to: live video feeds; video feeds (e.g., that includepreviously recorded content), and the like as known to one skilled inthe field of art. A live video feed as described herein is intended tocover live streaming instances where video data, among other types ofdata (e.g., audio), is processed as a data stream (e.g., video stream).Users of a video discussion application/service may post videos, videoclips, feeds, etc., in response to a topic that is posted fordiscussion. For instance, a teacher may post a topic for students torespond to for an educational assignment. For ease of understanding, anon-limiting example is described in the present disclosure thatpertains to a teacher posting a topic for a film study class (“FilmStudy 101”), where a group of students are posting videos in response toa posted assignment. For ease of understanding, this high-level examplemay be referenced in other portions of the present disclosure. Intraditional implementations of video discussion applications/services,users (students) would be required to manually stitch together multiplevideo clips to create a response to an assignment. Above that, postedvideo clips have been traditionally treated as their own objects ratherthan a combined data object. This poses technical challenges when usersare collaboratively editing content and manually attempting to combinecontent into a final product for assignment submission. For ease ofunderstanding, a non-limiting example of a video discussionapplication/service is Flipgrid® where back-end processing (e.g.,server-side processing) behind a video discussion application andfront-end representations (e.g., an adapted GUI) bring onlinecollaboration to application/service components (e.g., Flipgrid® cameraapplication/service).

A collaborative workspace provides features for managing usercollaboration to enable task execution within an application/service. Anexemplary collaborative workspace fosters synchronous and/orasynchronous user collaboration via a group space that is accessible tomultiple users. In some examples, collaboration, through thecollaborative workspace, occurs where two or more users who concurrentlyaccess the collaborative workspace. In alternative examples, users mayutilize the collaborative workspace in an asynchronous manner to conductuser collaboration. The collaborative workspace further provides a topicfor the at least two users to respond to by providing content such asvideo feeds. For instance, a collaborative workspace of a videodiscussion application/service is configured to provide GUI featuresthat enable users to conduct a video discussion of one or more topicsand further provide users with the ability to synchronously and/orasynchronously manage content for posting/submission. Within anexemplary video discussion application/service, numerous features areavailable to users to aid with video recording and editing. Non-limitingexamples of such features comprise but are not limited to: cameraoptions to manage content addition (e.g., recording of a video clip,uploading of a video clip, mirroring of video, audio control, videocontrol, recording of a screen, image snapshots); camera effect options(e.g., adding of filters, frames, emojis, text, drawings, addition ofboards such as whiteboards, resizing and cropping); and features fortopic management and/or note addition (e.g., the addition of stickynotes that are displayed for users within a collaboration workspace).Further, in the present disclosure, a new effect feature is alsointroduced allowing users to create a dueting of a video clip. Duetingenables users to record a video clip (or concurrently play apre-recorded video clip) simultaneously with the playback of anothervideo clip. Essentially, users can build off another user's video byrecording their own video(s) alongside another video as it plays,thereby providing a new layer of creativity and user interaction.Notably, an improved GUI is adapted to enable users to collaborativelycreate a dueting clip (e.g., multiple users can create a duet with avideo engaged in playback), including GUI features that enable automaticinitiation of a collaborative duet and control over which users areinvolved in the dueting clip.

Through the present disclosure, an exemplary collaborative workspaceenables users to join the same video creation session (e.g.,collaborative session) while fostering an environment that allowsmultiple users to join and record video clips in response to a postedtopic. In some examples, video clips can be recorded at the same timewhere the collaborative workspace is configured to intelligently (andautomatically) create an arrangement of those video clips as a singlemedia object. For instance, a teacher may post an assignment forstudents via a web application/service (e.g., Flipgrid® camera), where agroup of students can collaboratively create and/or upload video clipsto create a response to the assignment for submission and review by theteacher. Video clips added/uploaded by users are automatically combinedto create a draft for assignment submission, which can further becollaboratively edited by a group of users in association with acollaborative session of the collaborative workspace. Collaborativesessions described herein may pertain to synchronous and/or asynchronoususer collaboration through an exemplary collaborative workspace and/orother application/service endpoints. Non-limiting visual examples of acollaborative workspace are provided in FIGS. 3A-3L.

Processing operation 202 comprises transmission of data that enablesrendering of GUI representations of a collaborative workspace for eachuser of a collaborative session. While GUI representations may berendered for individual users of a collaborative session, thecollaborative workspace provides, across all GUI representations,real-time (or near real-time) updates of a state of a user communicationincluding generation of a collaborative media object and managementthereof. For instance, if a first user adds a video clip to acollaborative media object, a GUI menu providing a representation of thecollaborative media object would be updated across each of the GUIrepresentations in real-time (or near real-time) within thecollaborative workspace. Furthermore, an exemplary collaborativeworkspace is adapted to provide GUI elements/features that fostercollaboration between users for task completion (e.g., creating andsubmitting a collaborative media object). GUI representations of acollaborative workspace may further comprise updates as to a state ofuser access/interaction with a collaborative workspace (e.g., individualGUI representations for users thereof). For example, this may comprisepresentation of user presence indications; dynamic timelines of useractivity including a state of a collaborative media object and renderingof data insights to assist with user interaction with a collaborativeworkspace and/or collaborative media object.

Flow of method 200 may proceed to processing operation 204, where signaldata may be received indicating user interactions with representationsof a collaborative session within a collaboration workspace. Asindicated in the foregoing description, including the description ofsystem diagram 100 (FIG. 1), signal data may be collected and analyzedto aid processing described herein. Non-limiting examples of signaldata, which may be collected and analyzed, have been described in theforegoing description. While processing operation 204 refers to thereceipt of signal data pertaining to user interactions with acollaborative workspace, it is to be recognized that signal dataindicating user interactions within the collaborative workspace may beutilized to detect and retrieve other types of signal data previouslydescribed. For example, signal data through a video discussionapplication/service may indicate that a plurality of users arecollaborating in a collaborative session provided through a videodiscussion application/service. This may be a trigger to obtain signaldata pertaining to user accounts associated with a collaborative sessionof an application/service (e.g., video discussion application/service);device-specific access data (e.g., what computing devices are beingutilized to access the collaborative session currently or in the past);application-specific or service-specific usage data from otherapplications/services (e.g., of a distributed software platform),telemetry data or a combination thereof. Any types of signal data, aloneor in combination, may be utilized to aid importance/relevance rankingprocessing to generate determinations including but not limited toautomatic determinations with respect to: generation of collaborativemedia objects including arrangement of content thereof, editingcapabilities of users (e.g., user settings for collaborativeediting/viewing); generation of presence indications for interactionswith a collaborative media object during one or more collaborativesessions; generation of a dynamic timeline providing temporalrepresentation of user interactions with a collaborative media object;and generation and provision of data insight representations for acollaborative workspace (e.g., for interaction with a collaborativemedia object), among other examples. Importance ranking processing orrelevance ranking processing may be executed by one or more trained AImodels (e.g., via a component for implementation of trained AIprocessing 108), where results of ranking processing may beautomatically applied to automatically generate determinations usable toupdate a collaborative workspace (including creation of a collaborativemedia object).

Processing operation 204 comprises detecting signal data pertaining tousers that are actively accessing a collaborative workspace as well asusers that are registered for/associated with a collaborative workspacewho may subsequently access the collaborative workspace at a later pointin time. For instance, user-specific signal data pertaining to a userwho is not synchronously accessing a collaborative workspace, butassociated therewith, may be useful to provide updates to that user(e.g., through a video discussion application/service upon subsequentaccess or through a different application/service endpoint) when anotheruser modifies an exemplary collaborative media object. Signal dataindicating a history of user access to collaborative workspace mayfurther be useful to help generate collaborative media objects andprioritize or arrange content portions thereof.

As indicated in the foregoing description, signal data indicating userinteractions with representations of a collaborative session within acollaboration workspace may comprise user actions to create or add videoclips to a collaborative session of the collaborative workspace. Forinstance, a component of a video discussion application/service mayreceive submissions of video feeds in response to a posted topic. Videofeeds can be recorded live through the collaborative workspace (e.g.,during a collaborative session) and/or uploaded by users within thecollaborative session. The collaborative workspace is configured todetect and analyze added content and automatically generate a singlemedia object (e.g., collaborative media object) from one or more addedvideo feeds. Portions of the single media object can be edited (e.g.,within the collaborative workspace) but the entirety of thecollaborative media object is a treated a single media object.

In one example, processing operation 204 comprise detecting recording ofa first live video feed from a first device (e.g., user computingdevice) associated with a first user of a collaborative sessionpresented through the collaborative workspace. The first live video feed(or live camera feed) may be recorded within the collaborative workspacein response to a posted topic that is associated with the collaborativeworkspace. For instance, the first live video feed may be recordedthrough a camera feature provided through the video discussionapplication/service, where a GUI representation of the camera featuremay be presented to a user through a GUI (e.g., representation of thecollaborative workspace) of the video discussion application/service. Infurther examples, one or more users (e.g., the same user or another userassociated with a collaborative session) may add additional video feeds(e.g., live video feeds or any type of video feed included apre-recorded video feed) to the collaborative workspace. For example,similar to the recording of the first live video feed, a second livevideo feed from a second device (e.g., user computing device) associatedwith a second user of a collaborative session may be recorded within thecollaborative workspace. Similarly, the second live video feed may berecorded within the collaborative workspace in response to the postedtopic associated with the collaborative workspace. In further examples,additional video feeds (e.g., live video feed or clip of previouslyrecorded video) may be added to the collaborative workspace by one ormore users of a collaborative session. For example, a third video feedmay be recorded by or added from a third device associated with a thirduser of the collaborative session. Alternatively, one of the first orsecond users may add the third video feed to the collaborativeworkspace. Similarly, the third video feed may be posted in response tothe topic associated with the collaborative workspace. User interactionswithin a collaborative workspace may further add additional contenttypes in response to a topic. In some examples, those additional contenttypes may further be added as content portions of a collaborative mediaobject. Essentially, the collaborative media object comprises multiplevideo feeds in aggregate but may further combine the same with othercontent portions of various content types. Non-limiting examples ofother content types for inclusion within a collaborative media objectcomprise but are not limited to: images, memes and/or screenshots;message content; emojis or icons; electronic documents (e.g., wordprocessing documents, notes documents, spreadsheets, slide-basedpresentations); handwritten input; audio clips; data objects;advertisements; and executable software content, among other examples.

In further examples, signal data indicating user interactions withrepresentations of a collaborative session within a collaborationworkspace may comprise user actions to edit a created collaborativemedia object. Modification-type interactions of a collaborative mediaobject may occur after a collaborative media object is generated andrendered within a GUI representation of the collaborative workspace.Non-limiting examples of such actions comprise but are not limited to:editing an ordering/arrangement of content portions (e.g., video clips)in a representation of a collaborative media object; adding labelingtags, layers of content, etc., to portions of collaborative mediaobject; editing content portions of the collaborative media objectincluding trimming of frames of one or more video clips that areincluded as a content portion within a collaborative media object;retroactively applying camera options to manage content of a video clip;applying camera effect options; applying features for topic managementand/or note addition including adding or removing of topics/sub-topics,tags, etc., to/from a collaborative media object; creating a duetingclip using one or more video clips and/or other content portions of acollaborative media object; deleting of content portions of a renderedcollaborative media object; and applying of automated user actionspresented in data insights associated with the collaborative workspace,among other examples.

Flow of method 200 may then proceed to processing operation 206.Regardless of the number of video feeds and/or video clips that areadded to the collaborative workspace, a collaborative media managementcomponent (106 of FIG. 1), associated with the collaborative workspace,may automatically generate (processing operation 206) a collaborativemedia object. Processing for automatic generation (processing operation206) of the collaborative media object comprises automatically combiningadded video feeds/videoclips (and in some cases content portions ofother content types) into a single media object for presentation in thecollaborative workspace of the video discussion application or service.Execution of processing operation 206 may occur through a programmedsoftware module, a trained AI model or a combination thereof. Anexemplary collaborative media management component (or the like) may beconfigured to detect and analyze added content to a collaborativeworkspace and automatically execute processing operations to generate asingle media object (e.g., collaborative media object) from one or moreadded video feeds (or other content portions). Portions of the singlemedia object can be edited (e.g., within the collaborative workspace)but the entirety of the collaborative media object is a treated a singlemedia object. This is beneficial for not only review of a submission ofthe collaborative media object (e.g., by a teacher) but also for userswho want to export or transfer the collaborative media object to work inother applications/services.

In one example, processing operation 206 comprises aggregating a clip ofthe first live video feed and a clip of the second live video feed in asequential order (and the third video feed in such examples) to createthe single media object. When additional video clips or content portionsare added, the collaborative media object can be updated compilingaggregated content into a single media object. Aggregation of videoclips (and other content portions) in a sequential order may evaluatetimestamp data associated with a video clip to determine an order orarrangement within the collaborative media object. In some instances,developers may set a specific attribute of timestamp data as thedelimiting factor in determining how to order video clips. For instance,a timestamp indicating when a video clip was added to the collaborativeworkspace (via recording or upload) may be the primary factor forordering video clips and/or content portions. In another example, atimestamp indicating when a video clip (or content portion) was recorded(or created) may be the primary factor for determining how to arrangecontent of the collaborative media object.

In further examples, trained AI processing may be applied toautomatically generate (processing operation 206) a collaborative mediaobject. Trained AI processing that executes processing operations tointelligently determine how to arrange the content (e.g., clips of videofeeds) for aggregation as a collaborative media object. For example,relevance ranking processing may generate relevance scores associatedwith each specific content portion (e.g., video clips) for inclusion ina collaborative media object. Generated relevance scoring may becomparatively used to prioritize video clips for an arrangement as asingle media object. For instance, the higher the relevance scoring thehigher the priority in the order of arrangement. In one example, arelevance score may be used to score a relevance of a content portion tothe posted topic that is associated with a representation of thecollaborative workspace (e.g., the topic that users are generating acollaborative media object to respond to). For instance, content andmetadata of a video clip may be analyzed to determine how relevant avideo clip is to the topic. In some examples, this may further factor inguidelines or parameters outlined in association with a topic which maybe analyzed and factored into relevance scoring. For example, a teachermay post requirements for a topical assignment (e.g., associated with acollaborative workspace), where relevance scoring scores how relevant astudent video clip is to the requirements in addition to the content ofthe posting. In yet another example, a group of users may havepreviously submitted assignments in the form of collaborative mediaobjects, where past history and user preferences from previousassignments may help determine how to arrange content for presentationas a collaborative media object. For instance, a trained AI model mayfocus on specific types of signal data that are most contextuallyrelevant to the purpose of creation of the collaborative media object(e.g., as an education assignment, work presentation, etc.). Inalternative examples, relevance scoring may be determined relative toslotted positions or sub-topics associated with a topic, where relevancescores may be generated for specific slots/positions (e.g., openingvideo clip, closing/summary video clip) or sub-topics and then orderedaccording to the relevance to the specific slots/positions (orsub-topics). In this way, relevance scoring is still evaluating contentportions relative to a topic, but further optimizes the manner by whichcontent is ordered, so that users are less likely to have to thenexecute manual actions to modify an arrangement.

Data that may be analyzed to generate relevance scoring may comprise butis not limited to: the various types of signal data previouslydescribed; content and metadata associated with a content portion (e.g.,video clip); user history regarding preferences (e.g., individual usersand/or a group or team of users) for creating collaborative mediaobjects; user account data including priority settings of users within agroup or team (e.g., team lead, project manager vs associate);guidelines and parameters set for a topic and/or assignment (e.g., by ateacher); or a combination thereof. Data and metadata associated with acollaborative workspace may be parsed and analyzed to identify a topic,guidelines requirements, associated users, etc., any of which may bepertinent to generating relevance scoring depending on how an AI modelis trained by developers. Content portions (e.g., video clips) as wellas other content of a collaborative workspace can be analyzed throughmeans known to one skilled in the art (e.g., optical characterrecognition; image recognition, natural language processing, etc.). Thatis, processing operations for obtaining data that is utilized togenerate relevance scoring (and further train an AI model) is known toone skilled in the field of art. Above what is traditionally known isthe application of the trained AI processing for generating uniquerelevance scoring and further applying exemplary relevance scoring togenerate unique determinations including the creation of a collaborativemedia object. A trained AI model may then use the results of therelevance scoring to arrangement content of a collaborative media objectas a single media object.

Once a collaborative media object is generated (processing operation206), flow of method 200 may proceed to processing operation 208. Atprocessing operation 208, data for rendering of the collaborative mediaobject may be transmitted for display in a GUI representation of thecollaborative workspace. For instance, this may occur in distributedexamples where a component is executing processing and transmits, over anetwork connection, data for rendering a representation of a GUI on aclient computing device (e.g., user computing device). As an example,transmission of data for rendering a collaborative media object, andrepresentations thereof, may comprise transmitting, to a client device,data for rendering the collaborative media object in a GUI presentingthe collaborative workspace (e.g., within the video discussionapplication/service). In alternative examples, processing to generate acollaborative media object, and representations thereof, may occurdirectly on a client device that is rendering a user representation ofthe collaborative workspace (e.g., representation of collaborativeworkspace for first user of a group of users).

Flow of method 200 may then proceed to processing operation 210. Atprocessing operation 210, a representation of a collaborative mediaobject is rendered in a representation of the collaborative workspace.In one example, a representation of a collaborative media object isrendered at a client computing device that is presenting arepresentation of a collaborative session (via a GUI representation of acollaborative workspace). In other examples, rendering of acollaborative media object may be generated via a first computing deviceand transmitted (e.g., over a network connection) to one or moreadditional computing devices for duplicating a rendering. Processingoperation 210 may comprise rendering portions of the collaborative mediaobject in a GUI associated with the collaborative workspace of the videodiscussion application or service, where the entirety of thecollaborative media object and/or portion thereof are independentlyeditable in a collaborative manner by the two or more users. In additionto rendering of portions of the collaborative media object, processingoperation 210 may comprise automatically generating, in thecollaborative workspace of the video discussion application or service,a separate GUI window that is specific to the collaborative media objectconfigured to enable editing management of the portions of thecollaborative media object by the two or more users. FIGS. 3A-3L of thepresent disclosure provide non-limiting examples of rendering of acollaborative media object within a representation of a collaborativeworkspace as well as GUI features/menus for management of acollaborative media object.

Flow of method 200 may then proceed to processing operation 212, wherepresence data (e.g., presence indications of users collaborativelymanaging a collaborative media object) is generated for indicating usercollaboration within the collaborative workspace. As previouslydescribed, users may be collaboratively working within the samecollaborative workspace through individual GUI representations thereofwhich capture editing from the perspective of an individual user. When auser is working on a specific task (e.g., editing a portion of acollaborative media object), they may temporarily lose track of whatother collaborators may be doing in other individual representations ofthe collaborative workspace. As such, a collaborative media managementcomponent may be configured to detect presence data for collaborativeusers and present presence indications (displayable in individual GUIrepresentation of the collaborative workspace) specifically pertainingto user actions relative to a collaborative media object. Presenceindications are GUI representations that identify specific users withina collaborative workspace and specific user action that is (or has beenperformed) relative to management of content portions of a collaborativemedia object. For instance, a first user may trim frames off a specificvideo clip, causing an updated version of the video clip (and thereforethe collaborative media object) to be generated. Other collaborativeusers may automatically see an updated version of the collaborativemedia object and wonder why a different version is displayed.Additionally, multiple users may be editing similar content portions atthe same time. It is useful to know what portions of the collaborativemedia object each user is editing in real-time (or near real-time) sothat user productivity and efficiency can be improved when editing acollaborative media object.

Processing operation 212 comprises detecting presence data of the atleast two users during interaction with portions of the collaborativemedia object displayed within the collaborative workspace of a videodiscussion application or service. This may occur through analysis ofsignal data detected for user actions within a collaborative workspace.Presence indications for respective users may then be generated fordisplay in a rendering of the collaborative workspace (e.g., individualGUI representations associated with specific users). Processingoperation 212 may further comprise transmitting data for rendering,within the collaborative workspace, presence indications forcollaborative users within the collaborative workspace. In additionalexamples, activity notifications may be generated and presented forusers which comprise a dynamic timeline providing temporalrepresentation of user interactions with a collaborative media object.For instance, since collaborative editing may occur in real-time, oneuser may add a video clip and then realize that another user may havemodified that video clip. As such, a collaborative media managementcomponent may be configured to generate dynamic media managementtimelines identifying user interactions (e.g., modification) with acollaborative media object as well as identification of a timing of whenan interaction occurred. This can aid in providing users with a fullerpicture of a collaborative session and even help identify a point thatan edit should be rolled back or returned to a previous version of thecollaborative media object.

Flow of method 200 may proceed to processing operation 214, whererepresentations of presence data is rendered within a representation ofthe collaborative workspace. Non-limiting examples of renderings ofrepresentations of presence data (e.g., presence indications and/ordynamic timelines of user activity relative to a collaborative mediaobject) are illustrated in FIGS. 3J and 3K.

Flow of method 200 then proceeds to decision operation 216. At decisionoperation 216, it is determined whether user action is received, throughthe collaborative workspace, that results in an update to thecollaborative media object. Non-limiting examples of updates to acollaborative media object have been described in the foregoingdescription. In technical instances where an update is received to thecollaborative media object, flow of decision operation 216 branches“YES” and processing of method 200 returns to processing operation 204.At processing operation 204, signal data is detected and analyzedregarding user actions within the collaborative workspace, for example,with respect to management of a collaborative media object. Processingof method 200 may re-execute processing operations (e.g., processingoperations 204-216) to determine how to update a representation of acollaborative media object.

As a non-limiting example, an edit to a video clip may be receivedthrough an interaction with the collaborative workspace of the videodiscussion application or service. The edit may be made by acollaborative user (e.g., second user) that is different from the user(e.g., first user) that posted the video clip. As a result of thatcollaborative edit, a representation of the collaborative media objectmay be updated. Data for rendering an updated representation of thecollaborative media object may be transmitted for display in thecollaborative workspace after the collaborative edit.

In another non-limiting example, users may edit one or more portions ofa collaborative media object via a new effect feature that allows usersto create a dueting of a video clip. Dueting enables users to record avideo clip (or concurrently play a pre-recorded video clip)simultaneously with the playback of another video clip. Essentially,users can build off another user's video by recording their own video(s)alongside another video as it plays, thereby providing a new layer ofcreativity and user interaction for generation of a collaborative mediaobject. Notably, an improved GUI is adapted to enable users tocollaboratively create a dueting clip (e.g., multiple users can create aduet with a video engaged in playback), including GUI features thatenable automatic initiation of a collaborative duet and control overwhich users are involved in the dueting clip. As an example, a duetingrequest may be received through a GUI of a representation of acollaborative workspace, where the dueting request is a request to addone or more video feeds (or other content portions) to the collaborativemedia object for simultaneous playback with a video clip. Collaborationthrough the collaborative workspace enables multiple live video feeds tobe recorded concurrent with the playback of another video clip, whichenhances a collaborative media object.

Continuing the above discussion, GUI features may be presented throughan adapted GUI representation that enables users to automaticallyinitiate collaborative dueting via user interface feature selection. Adueting request can be automatically created through a GUI that mayenable users to select, through the GUI, multiple users for automaticinitiation of a dueting of one or more video clips (e.g., that are partof the collaborative media object). Processing of the dueting requestautomatically initiates recording of a dueting clip. Upon completion ofthe dueting clip, a representation of the collaborative media object maybe automatically updated to include the dueting clip (e.g., replace aprior version of a video clip with a dueting clip that includes theoriginal video clip). For instance, a dueting request may be receivedfrom a device associated with a first user, where the dueting requestcomprises a request to record a live camera feed of the first user and alive camera feed of one or more additional users (e.g., a second user)concurrent with a playback of a video clip/video feed already added tothe collaborative media object. A dueting clip may then automatically beinitiated that replaces a clip of a prior recorded video feed with thedueting clip to create an updated representation of the collaborativemedia object (e.g., single media object). Data for rendering the updatedrepresentation of the collaborative media object may then be transmittedfor rendering within a GUI representation of the collaborativeworkspace.

In technical instances where an update is not received to thecollaborative media object, flow of decision operation 216 branches “NO”and processing of method 200 proceeds to processing operation 218. Atprocessing operation 218, a version of the collaborative media object isstored for recall. In one example, processing operation 218 may compriseuser action that posts/submits the collaborative media object for reviewby one or more other users (e.g., a teacher). This may automaticallytrigger storage of a version of a collaborative media object for laterrecall. In another example, a user may execute explicit action to save acollaborative media object or transfer the same to a data storage (e.g.,local or distributed). In any such instances, a version of thecollaborative media object may be stored, for example, on a distributeddata storage associated with the video discussion application/service ora distributed data storage for a user account (or group of users, team)associated with a file hosting service. In further examples, processingoperation 218 may comprise processing operations that automatically saveand store a version of a collaborative media object that is current tothe collaborative workspace. In one example, a user action to export aversion of the collaborative media object to another application/serviceendpoint may be a trigger to automatically save and store a version ofthe collaborative media object for recall. In other instances, anymodification (or a threshold level of modification) may be a trigger toautomatically store a version of a collaborative media object. Duringuser collaboration, multiple versions of a collaborative media objectmay be generated (e.g., continuous update to the collaborative mediaobject) before a final version is ready for posting or submission.Tracking versions of the collaborative media object and storing the samemay be useful as users continue to collaboratively edit a collaborativemedia object and may want to roll back (or undo) an edit to thecollaborative media object. For instance, multiple edited versions of acollaborative media object may be generated and accessible through GUIof the collaborative workspace. In some examples, a dynamic timelineproviding temporal representation of modifications to the collaborativemedia object may be displayed for a group of users (e.g., through thecollaborative workspace). This may provide users with a clearer pictureof the state of the collaborative media object, when (and who) editedthe collaborative media object and further provide context informationfor reviewers (e.g., teachers) of the collaborative media object.

Flow of method 200 may then proceed to processing operation 220. Atprocessing operation 220, the collaborative media object may be recalledfor subsequent usage (e.g., presentation or transmission) by a user ofan application/service. For instance, one or more users may exit thecollaborative workspace and subsequently return for update to thecollaborative media object. A rendering of the collaborative mediaobject may be presented in a GUI representation of the collaborativeworkspace. In some examples, the collaborative media object may berecalled for editing and/or transport directly from a data storage(e.g., distributed data storage) rather than through a GUIrepresentation of a collaborative workspace. In alternative examples,recall (processing operation 220) of a collaborative media object mayoccur directly through a different application/service endpoint. Forinstance, an exemplary collaborative workspace of a video discussionapplication/service may be integrated for presentation within anotherapplication/service (GUI representation thereof). The present disclosureenables extensibility where different application/services may beadapted to include GUI features that provide automatic integration of acollaborative workspace (e.g., of a video discussionapplication/service) within a GUI of a respective application/service.For example, a group of users may be conducting an electronic meetingthrough a different application/service at the same time they areediting a collaborative media object via a video discussionapplication/service. Integrating a rendering of a GUI representation ofthe collaborative workspace improves processing efficiency and usabilityof application/services, where users would not have to constantly switchbetween GUI windows to manage a collaborative media object whileconducting an electronic meeting.

As indicated in the foregoing description, a collaborative workspace maybe accessed at a later point in time by users. In further examples, acollaborative workspace may be associated with more than one topic,where GUI features for topical control may enable users to viewdifferent topics and create different collaborative media objects fordifferent topics. As such, the flow of method 200 indicates thatprocessing may return back to processing operation 202, where arendering of the collaborative workspace (e.g., GUI representationthereof) can be subsequently provided for user access.

FIG. 2B illustrates exemplary method 250 related to automatic generationand management of data insights to aid user collaboration within acollaborative workspace, with which aspects of the present disclosuremay be practiced. As an example, method 250 may be executed across anexemplary computing system 501 (or computing systems) as described inthe description of FIG. 5. Exemplary components, described in method250, may be hardware and/or software components, which are programmed toexecute processing operations described herein. Non-limiting examples ofcomponents for operations of processing operations in method 250 aredescribed in system diagram 100. Processing operations performed inmethod 250 may correspond to operations executed by a system and/orservice that execute computer modules/programs, software agents,application programming interfaces (APIs), plugins, AI processingincluding application of trained data models, intelligent bots, neuralnetworks, transformers and/or other types of machine-learningprocessing, among other examples. In one non-limiting example,processing operations described in method 250 may be executed by acomponent such as a collaborative media management component 106 (ofFIG. 1) and/or a component for implementation of trained AI processing108. In distributed examples, processing operations described in method250 may be implemented by one or more components connected over adistributed network. For example, components may be executed on one ormore network-enabled computing devices, connected over a distributednetwork, that enable access to user communications.

Method 250 begins at processing operation 252, where data is providedfor rendering of a GUI representation of a collaborative workspace of avideo discussion application/service. Processing for rendering a GUIrepresentation of a collaborative workspace, including distributedprocessing examples, have been described in the foregoing descriptionincluding the description of method 200 (FIG. 2A) and are similarlyapplicable. In one example, processing operation 252 renders acollaborative workspace in a GUI representation of a video discussionapplication or service. An exemplary collaborative workspace has beenpreviously defined. In one example, the collaborative workspace isconcurrently accessed by at least two users and further provides a topicfor the at least two users to respond to by providing content includingvideo feeds. Alternative examples of processing operation 252 comprisetransmitting data for rendering of the collaborative workspace to aclient computing device that will subsequently render a GUIrepresentation of a video discussion application or service.

Flow of method 250 may proceed to processing operation 254, where signaldata is detected (collected) for analysis. Signal data may be collectedthrough programmed interfacing with computing devices,applications/services, etc., as known to one skilled in the field ofart. For instance, an API or the like may be utilized to collectapplication-specific signal data from a video discussionapplication/service and/or devices, other applications/services andassociated user accounts. Exemplary signal data comprisesapplication-specific signal data associated with a collaborativeworkspace (e.g., collaborative session provided through access to thecollaborative workspace of a video discussion application/service). Forexample, during a collaborative session, signal data collected may besignal data associated with at least two users of a collaborativesession. This may comprise signal data directly collected through aspecific application/service that provides the collaborative workspaceas well as other types of signal data collected in association with useraccounts that are part of a collaborative session provided via thecollaborative workspace. Non-limiting examples of signal data have beendescribed in the foregoing description. For instance, signal data thatmay be collected and analyzed comprises but is not limited to:device-specific signal data collected from operation of one or more usercomputing devices; user-specific signal data collected from specifictenants/user-accounts with respect to access to any of: devices, loginto a distributed software platform, applications/services, etc.; andapplication-specific data collected from usage of applications/services(e.g., via a collaborative workspace and/or other application/serviceendpoints). With respect to application-specific signal data, signaldata indicating user interactions of users within a collaborativeworkspace (e.g., of a video discussion application or service) may becollected and analyzed to determine context of users within acollaborative workspace. In some examples, trained AI processing may betrained to focus on specific types of user interactions such as thosepertaining to generation and management of a collaborative media object.This may lead to generation of data insights that further the creationof a collaborative media object as well as those that bring attention tofeatures of a video discussion application/service that furthercollaborative media object creation. In some examples, a user contextevaluation is generated collectively for user collaboration of two ormore users within the collaborative workspace. This type of evaluationmay more accurately capture a state of user collaboration within acollaborative workspace. However, in further examples, signal datapertaining to a user associated with a collaborative workspace maycomprise signal data through computing devices and/or otherapplications/services, which can help provide a clearer picture of acontext of a user relative to action within the collaborative workspace.

Method 250 may then proceed to processing operation 256. At processingoperation 256, one or more trained AI models may be applied. Anexemplary trained AI model is specifically adapted to generaterepresentations of data insights for management of a collaborative mediaobject within the collaborative workspace (e.g., of a video discussionapplication/service). Implementation of trained AI processing (e.g., atrained AI model) has been previously described in the presentdisclosure. Method 250 will now focus on specific processing operationsexecuted by trained AI processing, which may be implemented by one ormore components (e.g., component configured for implementation oftrained AI processing 108 of FIG. 1) through interfacing with acollaborative media management component 106 (of FIG. 1). At leastprocessing operations 258 to 264 describe specific processing operationsexecuted by a trained AI model (or models). However, it is to berecognized that trained AI processing can be applied to improveprocessing (e.g., automatic and intelligent learning processing) for anyprocessing operation described in the present disclosure. For example, atrained AI model may be configured to detect (collect) signal data foranalysis and data insight generation.

At processing operation 258, the trained AI model is applied to analyzesignal data detected/collected which is associated with users of acollaborative session (e.g., within a collaborative workspace). Analysis(processing operation 258) of signal data may compriseapplication-specific signal data pertaining to video discussionapplication/service. This may aid a determination of a context of users(user context) within a collaborative workspace. Analysis (processingoperation 258) of signal data may comprise parsing signal data toidentify specific types of signal data (e.g., user-specific signal data,device-specific signal data, application-specific signal data) includingtargeted types of application-specific signal data pertaining to acollaborative session accessed via a collaborative workspace of a videodiscussion application/service. One or more different types of signaldata may be analyzed by a trained AI model. Targeted types ofapplication-specific signal data comprise but are not limited to: useraccounts accessing a video discussion application/service (includinguser accounts associated with a distributed software platform); userpresence (current and/or past access as well as user accounts assignedto a specific collaborative session) within a collaborative workspace;topics associated with a collaborative session of a collaborativeworkspace; parameters or guidelines for content submission for acollaborative session; data pertaining to creating of a collaborativemedia object including content portions added to a collaborativeworkspace and/or relevant to a topic of a collaborative workspace; datapertaining to user activity within a collaborative workspace includingmanagement of a collaborative media object; user messages or notes(e.g., sticky notes added) within a collaborative workspace; signal datapertaining to the management of video feeds (including selection ofcamera features and effects); and data pertaining to integration of acollaborative workspace (e.g., of a video discussionapplication/service) within other applications/services, among otherexamples.

Once signal data, including application-specific signal data, isdetected, flow of method 250 may proceed to processing operation 260. Atprocessing operation 260, a user context may be determined for one ormore users relative to a collaborative workspace. An exemplary usercontext is determined (processing operation 260) based on analysis ofdetected signal data including the collected application-specific signaldata. A user context is intended to be a determination of an activitystate of users of a collaborative workspace whether a user is accessingthe collaborative workspace or not. An exemplary user context can beconfigured to be a representation of an individual user state, acollaborative user state (e.g., identifying an activity state ofmultiple users relative to one another); or a combination thereof. Inone or more examples, an activity state of users within a collaborativeworkspace may be defined relative to a state of a collaborative mediaobject. However, it is to be recognized that the user context evaluationmay comprise determination of user activity that does not pertain tocollaborative media object creation.

As indicated in the foregoing description, a common task through acollaborative workspace is the creation and management of acollaborative media object. User context may be an explicitdetermination of use activity (or inactivity) relative to a creationstate of a collaborative media object whether or not users are activelyaccessing a collaborative workspace. A state of a collaborative mediaobject is intended to be a point in the lifecycle of creation of acollaborative media object. This data may be useful to generatingcontextual insights that further the task of collaborative media objectcreation. Any state determination for a collaborative media object maybe generated and used to help gauge a current representation of useractivity within a collaborative workspace. For instance, lifecyclecreation states of a collaborative media object may comprise a firststate of a collaborative media object that may be a pre-creation state,for example, where a collaborative media object has yet to be created. Asecond state of a collaborative media object (e.g., lifecycle creationstate) may be a draft completion state (e.g., one or more versions of acollaborative media object have been created but not yet finalized). Athird state of a collaborative media object (e.g., lifecycle creationstate) may be a confirmation state, where a collaborative media objecthas been created, edited and is subsequently ready for submission. It isto be recognized that other types of state of a collaborative mediaobject may be created and managed without departing from the spirit ofthe present disclosure.

In further examples, determination of a user context may furthercontemplate user activity in other applications/services concurrent (orasynchronously) with access to the collaborative workspace. Forinstance, a user context determination may correlate any of the examplesof application-specific signal data described above with other types ofsignal data collected. This may be useful to identifying a more completepicture or understanding of a user context (relative to their workwithin a collaborative workspace). As an example, user account data of auser account that is accessing a collaborative workspace may be utilizedto determine past user activity associated with that user accountthrough a video discussion application/service and/or other types ofapplications/services (e.g., email, messaging, collaborationapplications/services and other types of productivityapplications/services). In further technical instances, signal data maybe analyzed to identify electronic documents or files that areconcurrently being accessed along with a collaborative workspace and/orwere previously accessed (or recently generated). For instance, a usermay have recently created a video clip that is pertinent for submissionto a posted topic, but that video clip may not yet be uploaded to thecollaborative workspace. Content and metadata of any type ofapplication/service may be analyzed (and correlated withapplication-specific signal data) to determine potentially relevantcontent that may further user productivity through the collaborativeworkspace. Additionally, device-specific signal data may be correlatedwith application-specific data to help identify user preferences,relevant content, etc., all of which may be specific to the device auser may be utilizing to access the collaborative workspace.

The trained AI model may then be adapted to generate (processingoperation 262) data insights for a collaborative workspace. Forinstance, in examples where a collaborative workspace is provided for avideo discussion application/service, processing operation 262 comprisesgenerating data insights that correspond with features of a videodiscussion application/service. This may bring attention to features ofa video discussion application/service that can be utilized to completea task relative to a collaborative media object (e.g., creation,editing, commenting, exporting, submission/posting). In some examples,data insights may comprise pre-determined content and messaging thatdirects a user to a specific feature and/or provides a GUI feature forautomated processing. In other examples, data insights may becontextually tailored for users and/or group of users, where contentincluded therein can make reference to specific users, topics, tasks,actions, electronic documents, etc. In some technical instances,relevance scoring processing may further be utilized to determine whichcontent to include in a generated data insight.

Processing operation 262 further comprises actions to filter/curate datainsights for contextual relevance. In some technical examples, aplurality of data insights may be generated and stored. Those datainsights may then be scored for relevance to determine which datainsights are most relevant to the user context within the collaborativeworkspace. In alternative examples, a relevance scoring is used todetermine which data insights to generate. In any applied example,trained AI processing is configured to execute a relevance evaluation(e.g., relevance determinations) that identifies relevant features of avideo discussion application/service relative to a user context of oneor more users within the collaborative workspace. With respect to arelevance evaluation, a trained AI model may be trained to generaterelevance scoring (e.g., relevance ranking processing) that scores therelevance of individual data insights to a user context and/or a stateof a collaborative media object. For instance, a determination is madeas to how relevant a feature of a video discussion application/serviceis to state of the collaborative media object (e.g., creating, editing,exporting, posting).

As a non-limiting example, relevance scoring for a data insight may bederived from analysis of collected signal data, an identified usercontext (individually and/or collaboratively), a state of acollaborative media object within the collaborative workspace, or anycombination thereof. An AI model may be trained to process anycombination of that data and generate a relevance determinationtherefrom. For instance, user context within the collaborative workspacemay be comparatively evaluated relative to states of a collaborativemedia object (e.g., a current state of the collaborative media object aswell as future state that is intended to be reached as a final productthat ultimately contemplates the parameters set forth for contentsubmission). It is noted that a trained AI model may be configured tofocus on a specific relevance scoring metric or may be trained tocollectively consider multiple relevance scoring metrics to determinecontextually relevant data insights for presentation.

In one example of relevance ranking processing, relevance scoring may begenerated for one or more data insights that scores a relevance of adata insight to a most recent user action (or group of user actions)within a collaborative workspace. In one specific example, a relevancescoring may score a relevance of a data insight to a most recent useraction that modified the collaborative media object. For instance, basedon a user adding a content portion to a collaborative media object, adata insight may be sent to users with a suggestion for editing thatcontent portion or re-arranging content portions of an automaticallygenerated collaborative media object. In yet another example, AImodeling may be trained to score a relevance of a data insight relativeto assignment instructions (or parameters), associated with thecollaborative workspace, for posting/submitting a video discussionresponse to the topic. As an example, an administrative user (e.g.,teacher) may post rules or guidelines pertaining to a submission that isto be received from a student (or group of students), where datainsights can be intelligently generated to help users comply withassignment instructions. In further examples, AI modeling may be trainedto score a relevance of a feature of the video discussion application orservice relative to content associated with a different application orservice. For instance, two group members may have been discussing, via amessaging application/service, an addition of a specific video clip totheir collaborative media object. A feature to help automatically importthat video clip and automatically update their collaborative mediaobject may be extremely helpful.

In some examples, a trained AI model may be configured to collectivelycontemplate a plurality of relevance scoring metrics in a relevancedetermination. For instance, relevance metrics may be generated for anyof the above identified technical scenarios, where an aggregaterelevance score may be generated for a specific data insight as to itscontextual relevance to a user context within a video discussionapplication/service. In some example, AI modeling may be trained toapply a specific weighting to specific types of relevance scoringmetrics. As an example, relevance of actions pertaining to userinteractions associated with a collaborative media object may beweighted higher than user actions with content of otherapplications/services.

Processing operation 262 may further comprise processing thatcurates/filters data insights based on evaluation of relevance scoringgenerated thereof. A threshold evaluation of relevance scoring may beapplied to determine the most contextually relevant data insights for adetermined user context. Thresholds may be set by developers, wherevalues may vary without departing from the spirit of the presentdisclosure. One or more data insights may be selected/curated forrepresentation generation based on a threshold analysis of generatedrelevance scoring. In one example, a single data insight (e.g., highestscoring data insight based on relevance scoring) may be output forpresentation through a GUI of a collaborative workspace based onanalysis of relevance scoring. In other examples, N number of datainsights (e.g., any number of data insights) that exceed a thresholdrelevance score may be propagated for presentation or stored for recallwhen a user context changes. In even further examples, generated datainsights, whether they meet a threshold relevance scoring or not, may bestored for subsequent recall. As an example, a distributed data storage(e.g., knowledge repositories 110 of FIG. 1) may be utilized to managestorage of data insights and/or generated representations thereof. Whilea data insight may not be relevant to a current user context, that samedata insight may be more relevant to a future user context. Storing datainsights (and/or representations thereof) may improve processingefficiency during subsequent contextual analysis of a collaborativeworkspace. As referenced in the foregoing description, differentrepresentations of a collaborative workspace may be presented fordifferent users. If multiple users are concurrently accessing acollaborative workspace, there may be multiple representations of thesame collaborative workspace being managed by a collaborative mediamanagement component. It follows that different data insights may bemost relevant to different users, where different data insights may beselected for contextual relevance to different (user) representations ofa collaborative workspace.

Flow of method 250 may then proceed to processing operation 264. Atprocessing operation 264, a representation(s) of one or more datainsights may be generated. Representations of data insights may begenerated based on a result of analysis of a relevance evaluationpertaining to generated data insights. Processing for execution of arelevance evaluation of data insights has been described in theforegoing description including the description of processing operation262. Non-limiting examples of representations of data insights arevisually illustrated in FIGS. 4A-4J.

In some examples, representations of data insights may be generated(processing operation 264) in real-time (or near real-time) depending ona user context relative to the collaborative workspace. In otherexamples, representations of data insights may be generated (processingoperation 264) asynchronously from user access and then surfaced at themost relevant timing based on analysis of signal data received relativeto the collaborative workspace. Representations of data insights maycomprise not only content defining a contextually relevant data insightbut also GUI elements linked to features of a video discussionapplication/service that are provided through a collaborative workspace.For instance, a representation of a data insight may comprise aselectable GUI element, that when selected, provides automatic executionof a feature of the video discussion application or service. As anexample, multiple users may be collaboratively creating a collaborativemedia object, where selectable GUI elements may be presented to helpusers create (or add content to) the collaborative media object and/ormanage editing of the same. In further examples, representations of datainsights may enable automated import/export control of a collaborativemedia object across different applications/services. In even furtherexamples, representations of data insights may further enable users tointegrate a GUI version of the collaborative workspace within a GUI ofanother application/service. This is extremely beneficial to users whoare multi-tasking or are simply accessing content primarily in anotherapplication/service. In any example, relevance scoring results and/oranalysis of a user context (e.g., explicitly identifying a lifecyclecreation state of a collaborative media object) may be used to determinenot only content of data insight but also how to present the same (e.g.,whether to customize it for a specific user or provide a predeterminedtemplate for representation of a data insight). This type of analysisfurther pertains to determining whether to include specific GUIfeatures/elements that provide automatic actions for features of a videodiscussion application/service.

Flow of method 250 may proceed to processing operation 266. Atprocessing operation 266, data for rendering one or more representationsof data insights may be transmitted for display in a GUI representationof the collaborative workspace. For instance, this may occur indistributed examples where a component is executing processing andtransmits, over a network connection, data for rendering arepresentation of a GUI on a client computing device (e.g., usercomputing device). As an example, transmission of data for rendering arepresentation of a data insight, may comprise transmitting, to a clientdevice, data for rendering the one or more representations of datainsights in a GUI presenting the collaborative workspace (e.g., withinthe video discussion application/service). In alternative examples,processing to generate a representation of data insights, may occurdirectly on a client device that is rendering a user representation ofthe collaborative workspace (e.g., representation of collaborativeworkspace for first user of a group of users).

Flow of method 250 may then proceed to processing operation 268. Atprocessing operation 268, a representation of data insight (or multiplerepresentations thereof) is rendered in a representation of thecollaborative workspace. In one example, a representation of acollaborative media object is rendered at a client computing device thatis presenting a representation of a collaborative session (via a GUIrepresentation of a collaborative workspace). In other examples,rendering of a collaborative media object may be generated via a firstcomputing device and transmitted (e.g., over a network connection) toone or more additional computing devices for duplicating a rendering. Aspreviously indicated, FIGS. 4A-4J of the present disclosure providenon-limiting examples of rendering of representations of data insightsas well as GUI features/menus for management thereof.

After a representation of a data insight is rendered, flow of method 250may proceed to decision operation 270. At decision operation 270, it isdetermined whether a user interacts with a representation of a datainsight. In examples where a user interaction is not received withrespect to a representation of a data insight, flow of decisionoperation 270 branches “NO” and method 250 returns to processingoperation 252. At that point, processing of method 250 may proceed aspreviously described, for example, to execute new user contextevaluations. In some examples, a change to a user context may result inthe recall of a previously generated data insight and/or representationof a data insight. In examples where users are synchronously utilizingthe collaborative workspace, developers may set a predetermined timeperiod for re-execution of a user context analysis. In other examples,developers may set a trigger for re-execution of a user contextanalysis. For instance, the execution of a user action and/or an updateto a collaborative media object may be an automatic trigger tore-evaluate a user context associated with the collaborative workspace.As previously referenced, users may utilize the collaborative workspaceto collaborate asynchronously. As such, user contextual evaluations mayalso subsequently occur at different times.

In examples where a user interaction is received with a representationof a data insight, flow of decision operation 270 branches “YES” andmethod 250 proceeds to processing operation 272. At processing operation272, a GUI of a collaborative workspace is updated based on a result ofthe user interaction with the representation of the data insight. Forinstance, in examples where a GUI element is presented that isconfigured to execute an automatic action, an action is automaticallyexecuted when that GUI element is selected. Some non-limiting examplesof user interactions with representations of data insight are shown inFIGS. 4A-4J and further described in the accompanying description.

FIGS. 3A-3L illustrate exemplary processing device views associated withuser interface examples for an improved user interface that is adaptedfor generation and management of collaborative media objects, with whichaspects of the present disclosure may be practiced. FIGS. 3A-3L providenon-limiting front-end examples of processing described in the foregoingincluding system diagram 100, method 200 (FIG. 2A) and method 250 (FIG.2B).

FIG. 3A presents processing device view 300, illustrating a GUI 301 ofan application/service (e.g., a video discussion application/service)that is adapted to present an exemplary collaborative workspace. The GUI301 is configured to provide application command control and associatedGUI elements including those associated with management of one or moretopics for a collaborative session (of the collaborative workspace) aswell as management of video feeds, to be posted in response to a topic,through a video discussion application/service. For example, a topiccontrol feature 302 is provided to enable users to manage access tospecific topics provided through a collaborative workspace. In theexample shown, the topic control feature 302 is set to present acollaborative workspace representation for a topic “Film Study 101”,where a plurality of users may collaborate to create a video discussionposting/submission for a “Film Study 101” class. As shown in GUI 301,user presence status indications 303 indicate that a group of threeusers (“User 1”, “User 2 and “User 3”) are concurrently collaboratingwithin the collaborative workspace. As background, a teacher of the“Film Study 101” class may have posted an assignment for a group ofusers to respond to by providing a video discussion submission. This isan example of a video discussion application/service being utilized forremote learning (or electronic learning). However, as identified in theforegoing discussion, the present disclosure is not limited toeducational examples. An educational example is illustrated in FIGS.3A-3L for ease of understanding and explanation.

In the example shown in processing device view 300, the GUI 301 displaysa representation of the collaborative workspace as displayed to a firstuser (indicated by user identification 304). A camera representation 305for a first user (“User 1”) is provided within the GUI 301, whichprovides a representation of a live video feed (or live camera feed) forthe first user. When the first user selects to record a live video feedthrough the collaborative workspace, the camera representation 305 mayupdate to reflect a state of the first user via control over a cameradevice associated with a users' computing device. In some examples, aGUI feature for live collaborative video feeds 306 may be providedthrough the GUI 301. The GUI feature for live collaborative video feeds306 provides the live video feeds for other collaborative users (e.g.,“User 2”, “User 3”) of a collaborative session directly within arepresentation of the collaborative workspace (e.g., “User 1”).Additionally, a GUI menu for feature management 307 is presentedproviding features of functionality through the video discussionapplication/service for a user to manage video feeds that may be addedto the collaborative workspace. Non-limiting examples of features thatmay be included in the GUI menu for feature management 307 comprise butare not limited to: camera options to manage content addition (e.g.,recording of a video clip, uploading of a video clip, mirroring ofvideo, audio control, video control, recording of a screen, imagesnapshots); camera effect options (e.g., adding of filters, dueting,framing, emojis, text, drawings, addition of boards such as whiteboards,resizing and cropping); and features for topic management and/or noteaddition (e.g., the addition of sticky notes that are displayed forusers within a collaboration workspace). Furthermore, a GUI timeindicator 308 may be provided that provides timestamp data for therecording of live video feeds via the video discussionapplication/service. For instance, a live camera feeds may be recordedin set intervals (e.g., maximum length of recording is 1 minute), whereeach time a user initiates recording of a live camera feed a recordingcan be created up to that designated interval. Administrative users(e.g., teachers) of a collaborative workspace may set time intervals(e.g., a maximum length for recording of a video clip) that may beapplied to live video feed recording. In some alternative examples, noset time interval is predetermined for live video feed recording.

FIG. 3B presents processing device view 310, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 300 (FIG. 3A). In the example shown inprocessing device view 310, a live camera recording feature 311 isselected, via user action 312, from the GUI menu for feature management307. The user action 312 is a trigger to begin recording of a live videofeed (e.g., of the first user) via the video discussionapplication/service. As previously indicated, a maximum length of arecording of a live video feed may be preset according to a set intervalfor the application/service or via user preferences.

FIG. 3C presents processing device view 315, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 310 (FIG. 3B). In the example shown inprocessing device view 315, a camera representation 305 for the firstuser is updated to enable recording of a live video feed (live camerafeed) of the first user. An updated camera representation 316 ispresented that reflects a live video feed of the first user as beingrecorded. As can be seen in processing device view 315, the GUI timeindicator 308 indicates a recording time for recording of the live videofeed. In examples where a set interval is applied indicating a maximumrecording length, the recording will complete upon the time the maximumrecording time is reached (e.g., 1 minute). Should the user wish toquickly initiate recording of a subsequent live video feed, a quickrecord GUI feature 317 (e.g., “Next”) is presented for selection,overlaying the camera representation 305. Selection of the quick recordGUI feature 317 would automatically initiate another recording of a livevideo feed from directly with camera representation 305 without the userhaving to utilize the GUI menu for feature management 307.

FIG. 3D presents processing device view 320, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 315 (FIG. 3C). As can be seen in processingdevice view 320, a representation of the collaborative workspace, viathe GUI 301, has been changed to provide a representation of GUI 301from the perspective of a second user (“User 2”). This is reflected inthe update to the user identification 321 (identifying “User 2”).Furthermore, a GUI window 322 is automatically added to the GUI 301 toreflect the automatic generation of a collaborative media object. GUIwindow 322 provides access to a current state of a collaborative mediaobject. For example, GUI window 322 is automatically rendered andpresented in a GUI 301 in response to the addition of the live videofeed (“Clip 1-User 1”) recorded by user 1 as previously described withreference to FIGS. 3B and 3C. Added content portions of a collaborativemedia object may further be presented in GUI window 322. For instance,identification of a first live video feed 323 (“Clip 1-User 1”) ispresented within GUI window 322 to enable “User 2” to view and/or editthe first live video feed 323 and any additional video feeds (or contentportions) added to the collaborative media object.

Furthermore, processing device view 320 illustrates a continued examplewhere a second user is recording a live video feed to add to thecollaborative media object. A camera representation 324 (for “User 2”)is provided within the GUI 301, which illustrates a representation of alive video feed (or live camera feed) for the second user. The livecamera recording feature, presented in the GUI menu for featuremanagement 307, is selected via user action 325. The user action 325 isa trigger to begin recording of a live video feed (e.g., of the firstuser) via the video discussion application/service. An updated camerarepresentation 326 is presented that reflects a live video feed of thesecond user as being recorded. Upon completion of recording acollaborative media object may be automatically updated to reflect theaddition of the second live video feed (as shown in FIG. 3E).

FIG. 3E presents processing device view 330, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 320 (FIG. 3D). As can be seen in processingdevice view 330, a representation of the collaborative workspace, viathe GUI 301, has been changed to provide a representation of GUI 301from the perspective of a third user (“User 3”). This is reflected inthe update to the user identification 331 (identifying “User 3”). Acamera representation 332 (for “User 3”) is provided within the GUI 301,which illustrates a representation of a live video feed (or live camerafeed) for the third user. Furthermore, a GUI window 322 is automaticallyupdated in the GUI 301 to reflect an automatic update to thecollaborative media object. GUI window 322 provides access to a currentstate of a collaborative media object. For example, GUI window 322 isautomatically rendered and presented in a GUI 301 in response to theaddition of the second live video feed (“Clip 2-User 2”) recorded byuser 2 as previously described with reference to FIG. 3D. Added contentportions of a collaborative media object may further be presented in GUIwindow 322. For instance, identification of a first live video feed 323(“Clip 1-User 1”) and the second live video feed 333 (“Clip 2-User 2”)is presented within GUI window 322 to enable User 3 to view and/or editthe first live video feed 323 and/or the second live video feed 333. Ascan be seen in GUI window 322, the length (“1:39”) of the collaborativemedia object has been updated to reflect the aggregation of multiplelive video feeds as a single media object.

Moreover, processing device view 330 illustrates a continued examplewhere a third user is adding a third video feed to the collaborativemedia object. For instance, the third video feed may have beenpreviously recorded (e.g., via the collaborative workspace or anotherapplication/service). An options feature, presented in the GUI menu forfeature management 307, is selected via user action 334. The user action334 is a trigger to render a GUI sub-menu providing additional optionsfor managing video feeds within the collaborative workspace. Arepresentation of a GUI sub-menu is illustrated and further described inthe description of FIG. 3F.

FIG. 3F presents processing device view 335, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 330 (FIG. 3E). A GUI feature sub-menu 336 ispresented in processing device view 335, for example, in response to areceipt of user action 334 (FIG. 3E). The GUI feature sub-menu 336comprises a plurality of features for management of video feeds within acollaborative workspace. In the example shown in processing device view335, a user action 337 is received that selects a GUI feature 338configured to trigger upload of a video clip to the collaborativeworkspace. In response to the user action 337, selecting GUI feature338, additional GUI prompts (not shown) may be presented for a user toguide the user with uploading a video clip to the collaborativeworkspace.

FIG. 3G presents processing device view 340, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 335 (FIG. 3F). Processing device view 340illustrates the automatic update of a collaborative media object, whichoccurs based on a result of receiving an uploaded video clip (“FilmNoir”) from “User 3.” The GUI window 322, providing management of thecollaborative media object, is automatically updated in the GUI 301 toreflect an automatic update to the collaborative media object. GUIwindow 322 provides access to a current state of a collaborative mediaobject. For example, GUI window 322 is automatically rendered andpresented in a GUI 301 in response to the addition of the third videofeed (“Film Noir-User 3”). Added content portions of a collaborativemedia object may further be presented in GUI window 322. For instance,identification of a first live video feed 323 (“Clip 1-User 1”), thesecond live video feed 333 (“Clip 2-User 2”), and the third video feed341 (“Film Noir-User 3”) is presented within GUI window 322 to enableuser 3 to view and/or edit the first live video feed 323, the secondlive video feed 333 and/or the third video feed 341. As can be seen inGUI window 322, the length (“2:39”) of the collaborative media objecthas been updated to reflect the aggregation of multiple live video feedsas a single media object. Moreover, a user action 342 may be receivedthat selects the GUI window 322 to provide further management of thecollaborative media object. As shown in processing device view 345 (FIG.3H), a sub-menu of the GUI window 322 is presented that enables users tomanage content portions of the collaborative media object.

FIG. 3H presents processing device view 345, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 340 (FIG. 3G). As shown in processing deviceview 345, a GUI sub-menu 346 configured for collaborative media objectmanagement is presented. As referenced in the foregoing, presentation ofthe GUI sub-menu 346 may automatically occur in response to a receipt ofuser action 342 (FIG. 3G). An ordered (or arranged) frame representation347 of content portions, comprising a single media object, is presentedin GUI sub-menu 346. As previously indicated, processing to generate acollaborative media object may arrange content portions (e.g., addedvideo clips and/or other content portions) for representation as asingle media object. When the collaborative media object being executed,a seamless representation of content (e.g., presentation of added videoclips in a sequential order) occurs as if all the content portions arestitched together. Processing operations for arranging/ordering contentportions of a collaborative media object have been described in theforegoing description, including the description of method 200 (FIG. 2).

As shown in ordered frame representation 347, individual frames of addedvideo clips are presented in a sequential order. This enables users toview and edit individual frames of content portions of the collaborativemedia object. As such, users can collaboratively edit/modify anyportions (or sub-portions) of the single media object. A GUI feature 348configured for frame indication provides a visual identifier as to wherea focus is when editing frames of the collaborative media object. Forinstance, if a user selects an editing feature from a GUI menu ofediting features 350, the GUI feature 348 configured for frameindication clearly identifies what frame would be edited. In alternativeexamples, a user may manually select a specific frame and subsequentlyselect an editing feature from the GUI menu of editing features 350 toapply an edit.

Furthermore, GUI sub-menu 346 may further provide correspondingindications of the content portions that make up the collaborative mediaobject. While ordered frame representation 347 shows an order ofindividual frames that collectively form the single media object,ordered clip representation 349 shows a representation of video clips(and/or other content portions) that comprise the collaborative mediaobject. Ordered clip representation 349 enables users to edit theentirety of a video clip in a single action as opposed to modifyingindividual frames of each of the video clips presented. For instance, auser may wish to remove a video clip or modify an order/arrangement ofthe video clip within the collaborative media object. An adapted GUI 301makes it easier to work with the entirety of a video clip rather thanselecting individual frames.

Processing device view 345 further shows receipt of user actions (351and 352) to delete a video clip from the collaborative media object. Forinstance, a third user (“User 3”) may wish to delete the video clipadded by the second user (“Clip2-User 2”). A first user action 351 isreceived to select the second video clip (“Clip2-User 2”) from theordered clip representation 349, and a second user action 352 isreceived that selects a delete feature 353 from the GUI menu of editingfeatures 350. A result of processing of those user actions is presentedin FIG. 3I.

FIG. 3I presents processing device view 355, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 345 (FIG. 3H). To further illustrate thecollaborative nature of the collaborative workspace, a GUIrepresentation of the collaborative workspace is returned to arepresentation provided to the first user (“User 1”). In the exampleshown in processing device view 355, the collaborative media object isupdated to remove (“Clip2-User 2”) based on receipt of user actions byUser 3. This update is immediately reflected in the GUI representationprovided to “User 1”. A representation of a GUI window 356 formanagement of the collaborative media object illustrates the presentstate of the collaborative media object as comprising (“Clip1-User 1”)and (“Film Noir-User 3”).

Additionally, processing device view 355 illustrates receipt of useractions to create a dueting clip for the collaborative media object. Forinstance, a third video clip (“Film Noir-User 3”) or frames thereof canbe selected via user action 357 in conjunction with a user action 358that selects a duet feature 359 from the GUI menu of editing features350. For instance, user actions 357 and 358 may be continuous (orconsecutive) actions that identify specific content in which to apply adueting request. In alternative examples, the GUI feature 348 configuredfor frame indication can be utilized to indicate the content that a userwishes to use for creation of a dueting request. While not illustratedin processing device view 355, additional GUI features may be presentedthrough an adapted user interface to help users create a collaborativedueting that includes live video feeds from multiple userscollaboratively.

FIG. 3J presents processing device view 360, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 355 (FIG. 3I). In the example shown inprocessing device view 360, a collaborative dueting request is beingprocessed, where a dueting clip is being created that includes livevideo feeds from multiple users in a collaborative manner. For instance,a first user (“User 1”) may provide a dueting request to have live videofeeds from both “User 1” and “User 2” recording concurrent with playbackof a third video clip (“Film Noir-User 3”). A representation of a GUIwindow 361 for management of the collaborative media object is providedin view of that request, which provides real-time playback of the thirdvideo clip (“Film Noir-User 3”) simultaneously with the presentation oflive video feeds of the first user (live video feed 362 for “User 1”)and the second user (live video feed 363 for “User 2”). A user action364 is received to initiate recording of the dueting clip. Upon receiptof user action 364, the third video clip (“Film Noir-User 3”) may beautomatically executed along with recording of respective live videofeeds. In alternative examples, a selection of a duet feature, from theGUI menu of editing features 350, automatically triggers launch of adueting clip. For instance, users may be provided with visual cues(e.g., a countdown) that indicates when concurrent recording will begin.Arrow indication 365 is illustrated as a symbol for playback of thethird video clip simultaneously with the recording of live video feed362 and live video feed 363. Once a dueting clip is recorded, thecollaborative media object may be automatically updated to include thedueting clip (e.g., replacing a prior version of the third video clip).

FIG. 3K presents processing device view 370, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 345 (FIG. 3H). Returning back to a previousexample where the collaborative media object comprises three videoclips, a GUI representation of the collaborative workspace for a thirduser (“User 3”) is illustrated. Processing device 370 providesnon-limiting visual examples of exemplary presence indications, wherereal-time status updates for collaborative editing may be visuallyprovided to the third user through an adapted GUI 301 of thecollaborative workspace. As previously indicated, presence indicationsare GUI representations that identify specific users within acollaborative workspace and specific user action that is (or has beenperformed) relative to management of content portions of a collaborativemedia object. Presence indications may be visually presented in a GUIwindow 371 configured for management of the collaborative media object.Moreover, presence indications may be presented in addition to othercontent previously described with respect to GUI windows providingmanagement of the collaborative media object. For instance, a firstpresence indication 372 provides visual identification of activity of afirst user. In the example shown, a first user (“User 1”) may becurrently trimming frames off the second video clip (“Clip 2-User 2”)that is added by the second user (“User 2’). A second presenceindication 373 provides visual identification of activity of a seconduser. Concurrently, the second user (“User 2” may be viewing the thirdclip (“Film Noir-User 3”) that was added by the third user. Exemplarypresence indications help visually notify the third user ofcollaborative activity to help the third user determine its next task soas not to overlap with collaborative editing being applied by otherusers.

FIG. 3L presents processing device view 375, illustrating a continuedexample of the GUI 301 of the collaborative workspace (e.g., providedthrough a GUI of a video discussion application/service) that is shownin processing device view 370 (FIG. 3K). As referenced in the foregoingdescription (including the description of method 200 (FIG. 2), activitynotifications may be generated and presented for users which comprise atimeline providing temporal representation of user interactions with acollaborative media object. For instance, since collaborative editingmay occur in real-time, one user may add a video clip and then realizethat another user may have modified that video clip. As such, acollaborative media management component may be configured to generatedynamic media management timelines identifying user interactions (e.g.,modifications) with a collaborative media object as well asidentification of a timing of when an interaction occurred. This can aidin providing users with a fuller picture of a collaborative session andeven help identify a point that an edit should be rolled back orreturned to a previous version of the collaborative media object.

Processing device view 375 illustrates the presentation of a dynamictimeline 377 that identifies temporal representation of userinteractions (e.g., modifications) with a collaborative media object.For instance, an exemplary dynamic timeline 377 may be rendered as a GUIfeature of a GUI window 376 configured for management of thecollaborative media object. An exemplary dynamic timeline 377 maycomprise temporal representations of modification to a collaborativemedia object, where data included therein may comprise but is notlimited to: data indicating a timing of a modification; a description ofa content portion that was modified; and an identification of one ormore users involved in the modification. However, is it to be recognizedthat any type of determination, generated by an exemplary collaborativemedia management component (e.g., from analysis of signal data describedherein), may be rendered within a dynamic timeline 377.

In the example shown in processing device view 375, the dynamic timeline377 comprises a first temporal indication 378 providing data indicatinga first edit/modification to a collaborative media object. The firsttemporal indication 378 indicates that a first user (“User 1”) added avideo clip to the collaborative media object at the beginning of acollaborative session within the collaborative workspace. The secondtemporal indication 379 provides data indicating a subsequent edit tothe collaborative media object. For example, the second temporalindication 379 provides a temporal identification of the addition of acollaborative dueting clip (e.g., between “User 1” and “User 2”) to thecollaborative media object. The third temporal indication 380 providesan identification of a current update to the collaborative media object.For example, the third temporal indication 380 indicates that a thirduser (“User 3”) has just currently deleted a video clip (“Clip 2”) fromthe collaborative media object. As such, an exemplary dynamic timeline377 may provide a comprehensive temporal representation of modificationof a collaborative media object, which can continuously expand over timeas additional modifications occur to a collaborative media object. Insome alternative examples, a dynamic timeline 377 may further spanmultiple collaborative sessions. For instance, a collaborative workspacemay be associated with multiple topics, where the dynamic timeline 377can provide a comprehensive temporal representation across multipletopics and/or collaborative media objects. This may comprise technicalinstances where multiple topics are associated with a singlecollaborative media object as well as technical instances where each ofmultiple collaborative media objects are associated with an individualtopic.

FIGS. 4A-4J illustrate exemplary processing device views associated withuser interface examples for an improved user interface that is adaptedfor generation and management of data insights to aid user collaborationwithin a collaborative workspace, with which aspects of the presentdisclosure may be practiced. FIGS. 4A-4J provide non-limiting front-endexamples of processing described in the foregoing including systemdiagram 100, method 200 (FIG. 2A) and method 250 (FIG. 2B). The GUIexamples shown in FIG. 4A-4J build off of those examples previouslydescribed with respect to the examples provided in FIGS. 3A-3L.

FIG. 4A presents processing device view 400, illustrating a GUI, aspreviously shown as GUI 301 (FIG. 3A), which newly features an automaticdata insight notification that is provided through a GUI representationof a collaborative workspace. As previously described, an exemplary GUIis that of a video discussion application/service that is adapted topresent an exemplary collaborative workspace. Data insight notification402 is automatically presented through a GUI representation of thecollaborative workspace. Data insight notification 402 is a result ofback-end processing as previously described in the foregoing descriptionincluding the description of method 250 (FIG. 2B). For instance, datainsight notification 402 brings attention to a camera feature of thevideo discussion application/service while providing a suggestion for auser (“User 1”) to record a video clip to automatically initiategeneration of a collaborative media object. As previously described,analysis of signal data, including application-specific signal data, mayyield determinations as to specific topics associated with acollaborative session, specific user accounts associated with thecollaborative session and guidelines/instructions associated with asubmission of a collaborative media object via the video discussionapplication/service, among other examples. From the contextualdescription generated within data insight notification 402, it is clearthat a real-time contextual analysis was executed that is helpful tofurther task completion via the collaborative workspace. For example, astate of the collaborative media object was determined to be apre-creation state, where the collaborative media object has yet to becreated. Previous correspondence between a student (“User 1”) and ateacher may have further yielded a determination that “User 1” is thegroup leader for creation of a posting/submission via the videodiscussion application/service. As such, contextual insight generationcan greatly improve usability of applications/services such as videodiscussion applications/services.

FIG. 4B illustrates processing device view 405, presenting a GUI, aspreviously shown as GUI 301 (FIG. 3A), which newly features an automaticdata insight notification that is provided through a GUI representationof a collaborative workspace. Data insight notification 407 isautomatically presented through a GUI representation of thecollaborative workspace. Data insight notification 407 brings attentionto specific features and functionality of the collaborative workspace ofthe video discussion application/service. For example, data insightnotification 407 calls out the automatic presentation of a GUI windowfor management of a collaborative media object. Based on detection andanalysis of a user action (e.g., by “User 1”), Data insight notification407 is automatically provided for “User 2” through a GUI representationof the collaborative workspace. Data insight notification 407 furtherdirects a user as to how content can be automatically added to thecollaborative media object.

FIG. 4C illustrates processing device view 410, presenting data insightnotification 412. Data insight notification 412 is the result ofback-end context analysis that identifies content from anotherapplication/service that may be relevant to a topic of the collaborativeworkspace and generation of a collaborative media object. In the exampleshown in processing device view 410, it is determined that “User 3”recorded a video clip (“Film Noir”) prior to joining the collaborativeworkspace. Furthermore, to aid user productivity, an automatic GUIelement 414 is presented that is configured for automatic upload of thepreviously recorded video clip (“Film Noir”) to the collaborativeworkspace. Selection of the automatic GUI element 414 would result inexecution of an automatic action to update the collaborative mediaobject. Alternatively, the user could upload the video clip (“FilmNoir”) through a series of actions via a GUI menu for feature management(e.g., GUI menu for feature management 307 of FIG. 3A).

FIG. 4D illustrates processing device view 415, presenting data insightnotification 416. Data insight notification 417 brings attention to a“Trim” feature for editing of frames of an exemplary collaborative mediaobject. As previously described, application-specific signal data,including guidelines and parameters for posting/submitting acollaborative media object, may be analyzed and used to aid generationof content for data insights. In the example shown in data insightnotification 417, a length of a collaborative video submission isbecoming increasingly close to the time requirement set forth by ateacher who assigned the “Film Study 101” assignment. This is called outfor the users to make them aware of the guidelines/parameters set forcontent submission. Furthermore, contextual analysis of thecollaborative media object, among other types of signal data, revealsthat duplicate frames have been identified in the video clips thataggregate to form the collaborative media object. A suggestion forremoving some of duplicate frames is presented to “User 2”. This is acontextually relevant suggestion that results from relevance analysis ofa user context relative to the collaborative workspace.

FIG. 4E illustrates processing device view 420, presenting data insightnotification 422. Data insight notification 422 is an automatic datainsight configured to aid a user with automatic creating of acollaborative dueting clip. The processes for creation of a duetingrequest and collaborative dueting clips has been previously described inthe foregoing description. In processing device view 420, data insightnotification 422 provides GUI elements 424 that each correspond to arespect user that is concurrently accessing the collaborative workspace(e.g., users 1-3). Data insight notification 422 enables users toutilize GUI elements 424 to quickly select which users they would liketo include in a dueting request (including creation of a collaborativedueting request). In the example shown in processing device 420, “User1” executes a selection action 426 to elect to involve “User 3” in thecollaborative dueting request. Selection of creation GUI element 428would automatically initiate creation of a dueting clip involving “User3”. Additional users selected via GUI elements 424 would result in theautomatic initiation and processing of a collaborative dueting request.

FIG. 4F illustrates processing device view 430, presenting data insightnotifications related to management of different versions of acollaborative media object. Processing device view 430 presents aversion control GUI feature 432 (“CMO Version Management), whichprovides quick access to a GUI feature menu 436 that enables usercontrol over versioning of a collaborative media object. For instance, auser selection action 434 of GUI feature 432 would result in thepresentation of GUI feature menu 436 in a GUI of the video discussionapplication/service. In the example shown, GUI feature menu 436 furthercomprises a data insight that provides real-time (or near real-time)status updates as to the editing of a collaborative media objectincluding identifying that multiple versions of the collaborative mediaobject have been automatically saved on behalf of the group of users. Anautosave GUI feature 438 is further presented in GUI feature menu 436,where the autosave GUI feature 438 enables toggle control over whetherto automatically save versions of a collaborative media object. In someinstances, users may strictly prefer that a most recent version of thecollaborative media object be accessible.

Moreover, processing device view 430 presents additional GUI features,within the GUI feature menu 436, that enable further control overpresentation of a collaborative media object. Version selection GUIfeature 440 enables toggling/restoring of a version of the collaborativemedia object to a previous (or different) version of the collaborativemedia object. For instance, a user may not like the changes another usercollaboratively made to the collaborative media object. A user may wishto rollback the changes and restore a previous version (“Version 1”) ofthe collaborative media object. In a non-limiting example, the versionselection GUI feature 440 also includes a selectable feature that mayreveal a version listing GUI feature 442 which provides a full list ofall versions of the collaborative media object. In the example shown auser action 444 is executed that selects “Version 1” from the versionlisting GUI feature 442. This would automatically result in update ofthe version selection GUI feature 440. While a plurality of selectablefeatures is shown in GUI feature menu 436 for ease of viewing, it is tobe recognized that presentation of any of the described GUI features mayvary including presentation of a plurality of different GUI elementsand/or GUI windows.

FIG. 4G illustrates processing device view 445, presenting a dynamic GUItimeline 447 that visually presents temporal representation ofcollaborative editing related to a collaborative media object. Thedynamic GUI timeline 447 provides users with a visual representation ofuser activity, relative to the collaborative media object, within thecollaborative workspace. In further instances, a teacher, accessing thecollaborative workspace, can further evaluate (grade) user interactionsand participation in creation of the collaborative media object.

Processing device view 445 illustrates an alternative manner in whichversions of a collaborative media object may be represented to users ofthe collaborative workspace. In the example show, a first temporalindication 449 provides data related to generation of a collaborativemedia object within the collaborative workspace. A second temporalindication 451 provides data related to a first edit/modification of thecollaborative media object within the collaborative workspace (e.g., acollaborative session thereof). A third temporal indication 453 providesdata related to a subsequent edit/modification of the collaborativemedia object within the collaborative workspace (e.g., a collaborativesession thereof). In some examples, selectable GUI elements may also beprovided within the dynamic GUI timeline 447, which when selected,enable automatic return of the collaborative media object to arespective version (e.g., a previous version).

FIG. 4H illustrates processing device view 455, presenting a datainsight notification 457 that directs attention to an export GUI feature459 provided through a GUI of the video discussion application/service.Data insight notification 457 directs a user to additional features of aGUI window that is specific to management of a collaborative mediaobject within the collaborative workspace of a video discussionapplication/service. Export GUI feature 459 is configured toautomatically initiate a request to export the collaborative mediaobject for processing within another application/service (e.g., GUIthereof). In the example shown in processing device view 455, a userselection action 461 selects export GUI feature 459. This results in theautomatic presentation of a GUI listing of applications/services 463,where the user can select a specific application/service for export ofthe collaborative media object. For example, a user may select a wordprocessing application/service, which would result in the automaticexport of the collaborative media object to a word processing documentpresented through a GUI of the word processing application/service. Inexamples where a specific word processing document is selected, acollaborative media object may be automatically embedded within thatword processing document.

FIG. 4I illustrates processing device view 465, presenting a datainsight notification 467 directing attention to an integration GUIfeature 469 provided through a GUI of the video discussionapplication/service. When selected, the integration GUI feature 469 isconfigured to automatically integrate a GUI representation of thecollaborative workspace (of the video discussion application/service)into a GUI representation of another application/service. In the exampleshown in processing device view 465, contextual analysis of a usercontext yielded a determination that “User 3” is concurrently engaged inan electronic meeting (e.g., through MICROSOFT® TEAMS®) and continues toswitch back and forth between the video discussion application/serviceand a GUI for the electronic meeting. By selecting integration GUIfeature 469, via user selection action 471, the collaborative workspaceof the video discussion is automatically integrated into the GUIpresenting the electronic meeting as shown in processing device view 475(FIG. 4J).

FIG. 4J illustrates processing device view 475, presenting a visualrepresentation of an integration of a collaborative workspace (of avideo discussion application/service) within a GUI representation ofanother application/service endpoint (e.g., MICROSOFT® TEAMS®). As shownin processing device view 475, a GUI representation of the collaborativeworkspace 477 is presented within a GUI of another application/serviceendpoint. For ease of control over access to the collaborativeworkspace, a return GUI feature 479 is incorporated within the GUIrepresentation of the collaborative workspace 477. This may improveprocessing efficiency (e.g., reduction is processing of user actions) aswell as application/service usability by avoiding requiring users toclose/minimize/resize different GUI windows to access different GUIs.Selection of the return GUI feature 479 would result in the automaticreturn to a GUI window representation of the collaborative workspace asshown prior to integration within the different application/serviceendpoint.

FIG. 5 illustrates a computing system 501 suitable for implementingprocessing operations described herein related to generation andmanagement of collaborative media objects and data insights within anexemplary collaborative workspace, with which aspects of the presentdisclosure may be practiced. As referenced above, computing system 501may be configured to implement processing operations of any componentdescribed herein including exemplary collaborative media managementcomponents as previously described. As such, computing system 501 may beconfigured to execute specific processing operations to solve thetechnical problems described herein, which comprise processingoperations for intelligent and timely contextual analysis that is usableto automatically generate and management exemplary collaborative mediaobjects including integration across different applications/services.Computing system 501 may be implemented as a single apparatus, system,or device or may be implemented in a distributed manner as multipleapparatuses, systems, or devices. For example, computing system 501 maycomprise one or more computing devices that execute processing forapplications and/or services over a distributed network to enableexecution of processing operations described herein over one or moreapplications or services. Computing system 501 may comprise a collectionof devices executing processing for front-end applications/services,back-end applications/service or a combination thereof. Computing system501 comprises, but is not limited to, a processing system 502, a storagesystem 503, software 505, communication interface system 507, and userinterface system 509. Processing system 502 is operatively coupled withstorage system 503, communication interface system 507, and userinterface system 509. Non-limiting examples of computer system 501comprise but are not limited to: smart phones, laptops, tablets, PDAs,desktop computers, servers, smart computing devices including televisiondevices and wearable computing devices including VR devices and ARdevices, e-reader devices, gaming consoles and conferencing systems,among other non-limiting examples.

Processing system 502 loads and executes software 505 from storagesystem 503. Software 505 includes one or more software components (e.g.,506 a and 506 b) that are configured to enable functionality describedherein. In some examples, computing system 501 may be connected to othercomputing devices (e.g., display device, audio devices, servers,mobile/remote devices, gaming devices, VR devices, AR devices, etc.) tofurther enable processing operations to be executed. When executed byprocessing system 502, software 505 directs processing system 502 tooperate as described herein for at least the various processes,operational scenarios, and sequences discussed in the foregoingimplementations. Computing system 501 may optionally include additionaldevices, features, or functionality not discussed for purposes ofbrevity. Computing system 501 may further be utilized to execute systemdiagram 100 (FIG. 1), processing operations described in method 200(FIG. 2A), method 250 (FIG. 2B) and/or the accompanying description ofFIGS. 3A-3L and 4A-4J.

Referring still to FIG. 5, processing system 502 may comprise processor,a micro-processor and other circuitry that retrieves and executessoftware 505 from storage system 503. Processing system 502 may beimplemented within a single processing device but may also bedistributed across multiple processing devices or sub-systems thatcooperate in executing program instructions. Examples of processingsystem 502 include general purpose central processing units,microprocessors, graphical processing units, application specificprocessors, sound cards, speakers and logic devices, gaming devices, VRdevices, AR devices as well as any other type of processing devices,combinations, or variations thereof.

Storage system 503 may comprise any computer readable storage mediareadable by processing system 502 and capable of storing software 505.Storage system 503 may include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, cache memory or other data. Examples of storage mediainclude random access memory, read only memory, magnetic disks, opticaldisks, flash memory, virtual memory and non-virtual memory, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or other suitable storage media, except for propagatedsignals. In no case is the computer readable storage media a propagatedsignal.

In addition to computer readable storage media, in some implementationsstorage system 503 may also include computer readable communicationmedia over which at least some of software 505 may be communicatedinternally or externally. Storage system 503 may be implemented as asingle storage device but may also be implemented across multiplestorage devices or sub-systems co-located or distributed relative toeach other. Storage system 503 may comprise additional elements, such asa controller, capable of communicating with processing system 502 orpossibly other systems.

Software 505 may be implemented in program instructions and among otherfunctions may, when executed by processing system 502, direct processingsystem 502 to operate as described with respect to the variousoperational scenarios, sequences, and processes illustrated herein. Forexample, software 505 may include program instructions for executing oneor more collaborative media management component(s) 506 a as describedherein. Software 505 may further comprise application/servicecomponent(s) 506 b that provide applications/services as described inthe foregoing description such as applications/services that enableaccess to data usable to engage in user communications andapplication/services that enable users to engage in user communications,among other examples.

In particular, the program instructions may include various componentsor modules that cooperate or otherwise interact to carry out the variousprocesses and operational scenarios described herein. The variouscomponents or modules may be embodied in compiled or interpretedinstructions, or in some other variation or combination of instructions.The various components or modules may be executed in a synchronous orasynchronous manner, serially or in parallel, in a single threadedenvironment or multi-threaded, or in accordance with any other suitableexecution paradigm, variation, or combination thereof. Software 505 mayinclude additional processes, programs, or components, such as operatingsystem software, virtual machine software, or other applicationsoftware. Software 505 may also comprise firmware or some other form ofmachine-readable processing instructions executable by processing system502.

In general, software 505 may, when loaded into processing system 502 andexecuted, transform a suitable apparatus, system, or device (of whichcomputing system 501 is representative) overall from a general-purposecomputing system into a special-purpose computing system customized toexecute specific processing components described herein as well asprocess data and respond to queries. Indeed, encoding software 505 onstorage system 503 may transform the physical structure of storagesystem 503. The specific transformation of the physical structure maydepend on various factors in different implementations of thisdescription. Examples of such factors may include, but are not limitedto, the technology used to implement the storage media of storage system503 and whether the computer-storage media are characterized as primaryor secondary storage, as well as other factors.

For example, if the computer readable storage media are implemented assemiconductor-based memory, software 505 may transform the physicalstate of the semiconductor memory when the program instructions areencoded therein, such as by transforming the state of transistors,capacitors, or other discrete circuit elements constituting thesemiconductor memory. A similar transformation may occur with respect tomagnetic or optical media. Other transformations of physical media arepossible without departing from the scope of the present description,with the foregoing examples provided only to facilitate the presentdiscussion.

Communication interface system 507 may include communication connectionsand devices that allow for communication with other computing systems(not shown) over communication networks (not shown). Communicationinterface system 507 may also be utilized to cover interfacing betweenprocessing components described herein. Examples of connections anddevices that together allow for inter-system communication may includenetwork interface cards or devices, antennas, satellites, poweramplifiers, RF circuitry, transceivers, and other communicationcircuitry. The connections and devices may communicate overcommunication media to exchange communications with other computingsystems or networks of systems, such as metal, glass, air, or any othersuitable communication media. The aforementioned media, connections, anddevices are well known and need not be discussed at length here.

User interface system 509 is optional and may include a keyboard, amouse, a voice input device, a touch input device for receiving a touchgesture from a user, a motion input device for detecting non-touchgestures and other motions by a user, gaming accessories (e.g.,controllers and/or headsets) and other comparable input devices andassociated processing elements capable of receiving user input from auser. Output devices such as a display, speakers, haptic devices, andother types of output devices may also be included in user interfacesystem 509. In some cases, the input and output devices may be combinedin a single device, such as a display capable of displaying images andreceiving touch gestures. The aforementioned user input and outputdevices are well known in the art and need not be discussed at lengthhere.

User interface system 509 may also include associated user interfacesoftware executable by processing system 502 in support of the varioususer input and output devices discussed above. Separately or inconjunction with each other and other hardware and software elements,the user interface software and user interface devices may support agraphical user interface, a natural user interface, or any other type ofuser interface, for example, that enables front-end processing ofexemplary application/services described herein including rendering of:an improved GUI providing automatic generation of collaborative mediaobjects; application command control and GUI features to aid generationand management of collaborative media objects; notification ofautomatically generated data insights and representations thereof; andgeneration and management of dynamic data insight timelines, among otherexamples. User interface system 509 comprises a graphical user interfacethat presents graphical user interface elements representative of anypoint in the processing described in the foregoing description includingprocessing operations described in system diagram 100 (FIG. 1),processing operations described in method 200 (FIG. 2A), method 250(FIG. 2B) and/or the accompanying description of FIGS. 3A-3L and 4A-4J.A graphical user interface of user interface system 509 may further beconfigured to display graphical user interface elements (e.g., datafields, menus, links, graphs, charts, data correlation representationsand identifiers, etc.) that are representations generated fromprocessing described in the foregoing description. Exemplaryapplications/services may further be configured to interface withprocessing components of computing device 501 that enable output ofother types of signals (e.g., audio output) in conjunction withoperation of exemplary applications/services (e.g., presentationbroadcast service) described herein.

Communication between computing system 501 and other computing systems(not shown), may occur over a communication network or networks and inaccordance with various communication protocols, combinations ofprotocols, or variations thereof. Examples include intranets, internets,the Internet, local area networks, wide area networks, wirelessnetworks, wired networks, virtual networks, software defined networks,data center buses, computing backplanes, or any other type of network,combination of network, or variation thereof. The aforementionedcommunication networks and protocols are well known and need not bediscussed at length here. However, some communication protocols that maybe used include, but are not limited to, the Internet protocol (IP,IPv4, IPv6, etc.), the transfer control protocol (TCP), and the userdatagram protocol (UDP), as well as any other suitable communicationprotocol, variation, or combination thereof.

In any of the aforementioned examples in which data, content, or anyother type of information is exchanged, the exchange of information mayoccur in accordance with any of a variety of protocols, including FTP(file transfer protocol), HTTP (hypertext transfer protocol), REST(representational state transfer), WebSocket, DOM (Document ObjectModel), HTML (hypertext markup language), CSS (cascading style sheets),HTML5, XML (extensible markup language), JavaScript, JSON (JavaScriptObject Notation), and AJAX (Asynchronous JavaScript and XML), Bluetooth,infrared, RF, cellular networks, satellite networks, global positioningsystems, as well as any other suitable communication protocol,variation, or combination thereof.

The functional block diagrams, operational scenarios and sequences, andflow diagrams provided in the Figures are representative of exemplarysystems, environments, and methodologies for performing novel aspects ofthe disclosure. While, for purposes of simplicity of explanation,methods included herein may be in the form of a functional diagram,operational scenario or sequence, or flow diagram, and may be describedas a series of acts, it is to be understood and appreciated that themethods are not limited by the order of acts, as some acts may, inaccordance therewith, occur in a different order and/or concurrentlywith other acts from that shown and described herein. For example, thoseskilled in the art will understand and appreciate that a method couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, not all acts illustratedin a methodology may be required for a novel implementation.

The descriptions and figures included herein depict specificimplementations to teach those skilled in the art how to make and usethe best option. For the purpose of teaching inventive principles, someconventional aspects have been simplified or omitted. Those skilled inthe art will appreciate variations from these implementations that fallwithin the scope of the invention. Those skilled in the art will alsoappreciate that the features described above can be combined in variousways to form multiple implementations. As a result, the invention is notlimited to the specific implementations described above, but only by theclaims and their equivalents.

Reference has been made throughout this specification to “one example”or “an example,” meaning that a particular described feature, structure,or characteristic is included in at least one example. Thus, usage ofsuch phrases may refer to more than just one example. Furthermore, thedescribed features, structures, or characteristics may be combined inany suitable manner in one or more examples.

One skilled in the relevant art may recognize, however, that theexamples may be practiced without one or more of the specific details,or with other methods, resources, materials, etc. In other instances,well known structures, resources, or operations have not been shown ordescribed in detail merely to observe obscuring aspects of the examples.

While sample examples and applications have been illustrated anddescribed, it is to be understood that the examples are not limited tothe precise configuration and resources described above. Variousmodifications, changes, and variations apparent to those skilled in theart may be made in the arrangement, operation, and details of themethods and systems disclosed herein without departing from the scope ofthe claimed examples.

What is claimed is:
 1. A method comprising: rendering, in a graphicaluser interface (GUI) representation of a video discussion application orservice, a collaborative workspace that is accessed by at least twousers and further provides a topic for the at least two users to respondto by providing content including video feeds; detecting signal dataassociated with the at least two users, wherein the signal datacomprises application-specific signal data pertaining to userinteractions of the at least two users within the collaborativeworkspace of the video discussion application or service; applying atrained artificial intelligence (AI) model that is trained to generaterepresentations of data insights for management of a collaborative mediaobject within the collaborative workspace, wherein an application of thetrained AI model comprises execution of processing operations thatcomprise: analyzing the signal data, including the application-specificsignal data, using the trained AI model, determining a user context ofthe at least two users within the collaborative workspace based on ananalysis of the application-specific signal data, generating datainsights corresponding with features of the video discussion applicationor service based on a relevance evaluation that is derived fromanalyzing of: the signal data including the application-specific signaldata, the user context of the at least two users within thecollaborative workspace, and a state of a collaborative media objectwithin the collaborative workspace, wherein the collaborative mediaobject is a single media object that comprises an aggregation of two ormore of the video feeds added through the collaborative workspace of thevideo discussion application or service, and generating a representationof one or more data insights for the video discussion application orservice based on a result of the relevance evaluation; and presenting,in the GUI representation of the video discussion application orservice, the representation of the one or more data insights within thecollaborative workspace.
 2. The method of claim 1, wherein thegenerating of the data insights further comprises generating, as therelevance evaluation, relevance scoring for each of the data insightsthat scores a relevance of a data insight relative to a most recent useraction that modified the collaborative media object; and curating thedata insights based on a threshold evaluation of the relevance scoring,and wherein a data insight, used in the representation of one or moredata insights, is selected based on a result of the curating of the datainsights.
 3. The method of claim 1, wherein the generating of the datainsights further comprises generating, as the relevance evaluation,relevance scoring for each of the data insights that scores a datainsight relative to assignment instructions, associated with thecollaborative workspace, for posting a video discussion response to thetopic; and curating the data insights based on a threshold evaluation ofthe relevance scoring, and wherein a data insight, used in therepresentation of one or more data insights, is selected based on aresult of the curating of the data insights.
 4. The method of claim 1,wherein the generating of the data insights further comprisesgenerating, as the relevance evaluation, relevance scoring for each ofthe data insights that scores a data insight relative to a most recentuser action within the collaborative workspace; and curating the datainsights based on a threshold evaluation of the relevance scoring, andwherein a data insight, used in the representation of one or more datainsights, is selected based on a result of the curating of the datainsights.
 5. The method of claim 1, wherein the generating of the datainsights further comprises generating, as the relevance evaluation,relevance scoring for each of the data insights that scores a relevanceof a feature of the video discussion application or service relative tocontent associated with a different application or service; and curatingthe data insights based on a threshold evaluation of the relevancescoring, and wherein a data insight, used in the representation of oneor more data insights, is selected based on a result of the curating ofthe data insights.
 6. The method of claim 1, wherein the generating ofthe representation of the one or more data insights comprises generatinga selectable GUI element configured to provide an automatic execution ofa feature of the video discussion application or service, and whereinthe presenting of the representation of the one or more data insightscomprises presenting a corresponding data insight along with theselectable GUI element.
 7. The method of claim 6, wherein the selectableGUI element, upon selection, is configured to automatically export thecollaborative media object to another application or service.
 8. Themethod of claim 6, wherein the selectable GUI element, upon selection,is configured to automatically initiate integration of the collaborativeworkspace within a GUI of another application or service.
 9. A systemcomprising: at least one processor; and a memory, operatively connectedwith the at least one processor, storing computer-executableinstructions that, when executed by the at least one processor, causesthe at least one processor to execute a method that comprises:rendering, in a graphical user interface (GUI) representation of a videodiscussion application or service, a collaborative workspace that isaccessed by at least two users and further provides a topic for the atleast two users to respond to by providing content including videofeeds; detecting signal data associated with the at least two users,wherein the signal data comprises application-specific signal datapertaining to user interactions, of the at least two users, within thecollaborative workspace of the video discussion application or service;applying a trained artificial intelligence (AI) model that is trained togenerate representations of data insights for management of acollaborative media object within the collaborative workspace, whereinan application of the trained AI model comprises execution of processingoperations that comprise: analyzing the signal data, including theapplication-specific signal data, using the trained AI model,determining a user context of the at least two users within thecollaborative workspace based on an analysis of the application-specificsignal data, generating data insights corresponding with features of thevideo discussion application or service based on a relevance evaluationthat is derived from analyzing of: the signal data including theapplication-specific signal data, the user context of the at least twousers within the collaborative workspace, and a state of a collaborativemedia object within the collaborative workspace, wherein thecollaborative media object is a single media object that comprises anaggregation of two or more of the video feeds added through thecollaborative workspace of the video discussion application or service,and generating a representation of one or more data insights for thevideo discussion application or service based on a result of therelevance evaluation; and presenting, in the GUI representation of thevideo discussion application or service, the representation of the oneor more data insights within the collaborative workspace.
 10. The systemof claim 9, wherein the generating of the data insights furthercomprises generating, as the relevance evaluation, relevance scoring foreach of the data insights that scores a relevance of a data insightrelative to a most recent user action that modified the collaborativemedia object; and curating the data insights based on a thresholdevaluation of the relevance scoring, and wherein a data insight, used inthe representation of one or more data insights, is selected based on aresult of the curating of the data insights.
 11. The system of claim 9,wherein the generating of the data insights further comprisesgenerating, as the relevance evaluation, relevance scoring for each ofthe data insights that scores a data insight relative to assignmentinstructions, associated with the collaborative workspace, for posting avideo discussion response to the topic; and curating the data insightsbased on a threshold evaluation of the relevance scoring, and wherein adata insight, used in the representation of one or more data insights,is selected based on a result of the curating of the data insights. 12.The system of claim 9, wherein the generating of the data insightsfurther comprises generating, as the relevance evaluation, relevancescoring for each of the data insights that scores a data insightrelative to a most recent user action within the collaborativeworkspace; and curating the data insights based on a thresholdevaluation of the relevance scoring, and wherein a data insight, used inthe representation of one or more data insights, is selected based on aresult of the curating of the data insights.
 13. The system of claim 9,wherein the generating of the data insights further comprisesgenerating, as the relevance evaluation, relevance scoring for each ofthe data insights that scores a relevance of a feature of the videodiscussion application or service relative to content associated with adifferent application or service; and curating the data insights basedon a threshold evaluation of the relevance scoring, and wherein a datainsight, used in the representation of one or more data insights, isselected based on a result of the curating of the data insights.
 14. Thesystem of claim 9, wherein the generating of the representation of theone or more data insights comprises generating a selectable GUI elementconfigured to provide an automatic execution of a feature of the videodiscussion application or service, and wherein the presenting of therepresentation of the one or more data insights comprises presenting acorresponding data insight along with the selectable GUI element. 15.The system of claim 14, wherein the selectable GUI element, uponselection, is configured to automatically export the collaborative mediaobject to another application or service.
 16. A method comprising:transmitting, to a client computing device, data for rendering agraphical user interface (GUI) representation of a collaborativeworkspace within a video discussion application or service, wherein thecollaborative workspace is accessed by at least two users and furtherprovides a topic for the at least two users to respond to by providingcontent including video feeds; detecting signal data associated with theat least two users, wherein the signal data comprisesapplication-specific signal data pertaining to user interactions, of theat least two users, within the collaborative workspace of the videodiscussion application or service; applying a trained artificialintelligence (AI) model that is trained to generate representations ofdata insights for management of a collaborative media object within thecollaborative workspace, wherein an application of the trained AI modelcomprises execution of processing operations that comprise: analyzingthe signal data, including the application-specific signal data, usingthe trained AI model, determining a user context of the at least twousers within the collaborative workspace based on an analysis of theapplication-specific signal data, generating data insights correspondingwith features of the video discussion application or service based on arelevance evaluation that is derived from analyzing of: the signal dataincluding the application-specific signal data, the user context of theat least two users within the collaborative workspace, and a state of acollaborative media object within the collaborative workspace, whereinthe collaborative media object is a single media object that comprisesan aggregation of two or more of the video feeds added through thecollaborative workspace of the video discussion application or service,and generating a representation of one or more data insights for thevideo discussion application or service based on a result of therelevance evaluation; and transmitting, to the client computing device,data for rendering the representation of the one or more data insightswithin the collaborative workspace of the video discussion applicationor service.
 17. The method of claim 16, wherein the generating of thedata insights further comprises generating, as the relevance evaluation,relevance scoring for each of the data insights that scores a relevanceof a data insight relative to a most recent user action that modifiedthe collaborative media object; and curating the data insights based ona threshold evaluation of the relevance scoring, and wherein a datainsight, used in the representation of one or more data insights, isselected based on a result of the curating of the data insights.
 18. Themethod of claim 16, wherein the generating of the data insights furthercomprises generating, as the relevance evaluation, relevance scoring foreach of the data insights that scores a data insight relative toassignment instructions, associated with the collaborative workspace,for posting a video discussion response to the topic; and curating thedata insights based on a threshold evaluation of the relevance scoring,and wherein a data insight, used in the representation of one or moredata insights, is selected based on a result of the curating of the datainsights.
 19. The method of claim 16, wherein the generating of the datainsights further comprises generating, as the relevance evaluation,relevance scoring for each of the data insights that scores a datainsight relative to a most recent user action within the collaborativeworkspace; and curating the data insights based on a thresholdevaluation of the relevance scoring, and wherein a data insight, used inthe representation of one or more data insights, is selected based on aresult of the curating of the data insights.
 20. The method of claim 16,wherein the generating of the data insights further comprisesgenerating, as the relevance evaluation, relevance scoring for each ofthe data insights that scores a relevance of a feature of the videodiscussion application or service relative to content associated with adifferent application or service; and curating the data insights basedon a threshold evaluation of the relevance scoring, and wherein a datainsight, used in the representation of one or more data insights, isselected based on a result of the curating of the data insights.